Yonathan A. Arbel, The Readability of Contracts: Big Data Analysis, 21 J. Empirical Legal Stud. 927 (2024).

working-paper PDF of published journal article

Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 1, that the plain-language movement produced nearly 800 laws while resting on an asserted scientific readability crisis. The discussion situates this proposition within Abstract and the article's empirical reassessment of the plain-language movement. This is significant because a largely technocratic reform project achieved enormous legal reach without comparable public scrutiny of its empirical foundations. It connects to plain-language movement, consumer law, readability crisis, technocratic regulation, empirical foundations, legal reform.

the plain-language movement produced nearly 800 laws while resting on an asserted scientific readability crisis. This matters because a largely technocratic reform project achieved enormous legal reach without comparable public scrutiny of its empirical foundations.

plain-language movement
consumer law
readability crisis
technocratic regulation
empirical foundations
legal reform

The abstract states the article's main results; the body qualifies the sample, measurement, and causal implications in detail.

Source-draft page 1 (PDF page 1) develops this proposition in Abstract.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 1, that the article supplies the largest empirical test of plain-language claims by analyzing roughly two million contracts across industries and decades. The discussion situates this proposition within Abstract and the article's empirical reassessment of the plain-language movement. This is significant because scale and longitudinal breadth permit stronger benchmarking than studies built from a few hundred selected documents. It connects to big data, contract corpus, empirical legal studies, longitudinal analysis, machine learning, benchmarking.

the article supplies the largest empirical test of plain-language claims by analyzing roughly two million contracts across industries and decades. This matters because scale and longitudinal breadth permit stronger benchmarking than studies built from a few hundred selected documents.

big data
contract corpus
empirical legal studies
longitudinal analysis
machine learning
benchmarking


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 1, that consumer agreements have median readability scores close to those of daily news articles. The discussion situates this proposition within Abstract and the article's empirical reassessment of the plain-language movement. This is significant because the central empirical premise of a general consumer-contract readability crisis is not supported by the study's benchmark. It connects to consumer contracts, daily news, comparative readability, empirical benchmark, plain language, consumer protection.

consumer agreements have median readability scores close to those of daily news articles. This matters because the central empirical premise of a general consumer-contract readability crisis is not supported by the study's benchmark.

consumer contracts
daily news
comparative readability
empirical benchmark
plain language
consumer protection


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 1, that standard readability tools are unreliable, manipulable, and can differ by up to 4.6 grade levels on identical text. The discussion situates this proposition within Abstract and the article's empirical reassessment of the plain-language movement. This is significant because laws and compliance systems that treat a score as objective can reward implementation choice rather than genuine accessibility. It connects to readability formulas, measurement error, manipulability, Flesch-Kincaid, regulatory metrics, validity.

standard readability tools are unreliable, manipulable, and can differ by up to 4.6 grade levels on identical text. This matters because laws and compliance systems that treat a score as objective can reward implementation choice rather than genuine accessibility.

readability formulas
measurement error
manipulability
Flesch-Kincaid
regulatory metrics
validity


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 1, that the belief that most American adults cannot read beyond eighth grade is an unsupported myth. The discussion situates this proposition within Abstract and the article's empirical reassessment of the plain-language movement. This is significant because a false literacy baseline can misdirect consumer policy and obscure the needs of genuinely vulnerable readers. It connects to adult literacy, eighth-grade myth, consumer policy, vulnerable populations, evidence-based law, plain language.

the belief that most American adults cannot read beyond eighth grade is an unsupported myth. This matters because a false literacy baseline can misdirect consumer policy and obscure the needs of genuinely vulnerable readers.

adult literacy
eighth-grade myth
consumer policy
vulnerable populations
evidence-based law
plain language


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 1, that consumer protection should shift from superficial readability scores toward market structure, substantive terms, and targeted assistance. The discussion situates this proposition within Abstract and the article's empirical reassessment of the plain-language movement. This is significant because clear prose cannot cure exploitative bargains or structural failures, while personalized support can address actual reader needs. It connects to market structure, substantive fairness, targeted assistance, consumer vulnerability, smart readers, regulatory priorities.

consumer protection should shift from superficial readability scores toward market structure, substantive terms, and targeted assistance. This matters because clear prose cannot cure exploitative bargains or structural failures, while personalized support can address actual reader needs.

market structure
substantive fairness
targeted assistance
consumer vulnerability
smart readers
regulatory priorities


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 2, that the no-reading problem cuts against contract law's organizing ideal of assent. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because people who do not know terms may accept unfavorable deals and supply only paper-thin consent. It connects to no-reading problem, assent, contract theory, consumer welfare, autonomy, standard forms.

the no-reading problem cuts against contract law's organizing ideal of assent. This matters because people who do not know terms may accept unfavorable deals and supply only paper-thin consent.

no-reading problem
assent
contract theory
consumer welfare
autonomy
standard forms

The introduction frames the inquiry and policy stakes; it does not claim that clarity is valueless or that every contract is easy to understand.

Source-draft page 2 (PDF page 2) develops this proposition in Introduction.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 2, that consumers decline to read for many reasons unrelated to linguistic difficulty. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because nonnegotiability, trust, overload, timing, boredom, and the gap between written terms and firm behavior all weaken incentives to read. It connects to nonnegotiability, trust, information overload, firm behavior, reading incentives, consumer contracts.

consumers decline to read for many reasons unrelated to linguistic difficulty. This matters because nonnegotiability, trust, overload, timing, boredom, and the gap between written terms and firm behavior all weaken incentives to read.

nonnegotiability
trust
information overload
firm behavior
reading incentives
consumer contracts


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 2, that legal scholarship often treats arcane language as the leading cause of nonreading. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because the plain-language agenda depends on a causal bridge from textual complexity to consumer disengagement and harm. It connects to legalese, causal inference, nonreading, consumer harm, plain language, contract design.

legal scholarship often treats arcane language as the leading cause of nonreading. This matters because the plain-language agenda depends on a causal bridge from textual complexity to consumer disengagement and harm.

legalese
causal inference
nonreading
consumer harm
plain language
contract design


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 2, that the claimed readability gap is used to justify protection, competition, and informed-rights rationales. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because if the gap or the causal effects are misstated, multiple strands of consumer policy lose their empirical footing. It connects to readability gap, consumer protection, competition, rights knowledge, policy justification, empirical support.

the claimed readability gap is used to justify protection, competition, and informed-rights rationales. This matters because if the gap or the causal effects are misstated, multiple strands of consumer policy lose their empirical footing.

readability gap
consumer protection
competition
rights knowledge
policy justification
empirical support


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 3, that the plain-language movement is politically quiet but legally powerful. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because its limited public visibility masks extensive influence on lending, warranties, insurance, state contracts, and unfair-practice regulation. It connects to political visibility, Truth in Lending, warranties, insurance regulation, state law, consumer protection.

the plain-language movement is politically quiet but legally powerful. This matters because its limited public visibility masks extensive influence on lending, warranties, insurance, state contracts, and unfair-practice regulation.

political visibility
Truth in Lending
warranties
insurance regulation
state law
consumer protection

Source-draft page 3 (PDF page 3) develops this proposition in Introduction.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 3, that nearly 800 federal and state plain-language laws amount to a major consumer-protection revolution. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because the scale of enacted policy makes evaluation of effectiveness and opportunity cost urgent. It connects to statutory diffusion, consumer revolution, policy evaluation, regulatory scale, plain language, opportunity cost.

nearly 800 federal and state plain-language laws amount to a major consumer-protection revolution. This matters because the scale of enacted policy makes evaluation of effectiveness and opportunity cost urgent.

statutory diffusion
consumer revolution
policy evaluation
regulatory scale
plain language
opportunity cost


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 3, that the article tests four propositions: comparative readability, change over time, metric reliability, and adult literacy. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because separating these questions prevents a single intuition about difficult legal prose from standing in for multiple empirical claims. It connects to research design, comparative readability, time trends, measurement reliability, adult literacy, hypothesis testing.

the article tests four propositions: comparative readability, change over time, metric reliability, and adult literacy. This matters because separating these questions prevents a single intuition about difficult legal prose from standing in for multiple empirical claims.

research design
comparative readability
time trends
measurement reliability
adult literacy
hypothesis testing


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 3, that benchmarking contracts against texts adults voluntarily read is more informative than interpreting grade levels in isolation. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because a relative comparison avoids treating a contested formula output as a literal measure of human capacity. It connects to benchmarking, grade-level scores, daily reading, measurement interpretation, consumer behavior, comparative method.

benchmarking contracts against texts adults voluntarily read is more informative than interpreting grade levels in isolation. This matters because a relative comparison avoids treating a contested formula output as a literal measure of human capacity.

benchmarking
grade-level scores
daily reading
measurement interpretation
consumer behavior
comparative method


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 3, that the article contributes both a two-million-document corpus and machine-learning methods for contract research. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because methodological infrastructure can support future empirical work beyond the present readability dispute. It connects to research infrastructure, contract dataset, legal NLP, machine learning, open data, empirical methods.

the article contributes both a two-million-document corpus and machine-learning methods for contract research. This matters because methodological infrastructure can support future empirical work beyond the present readability dispute.

research infrastructure
contract dataset
legal NLP
machine learning
open data
empirical methods


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 4, that a universal and mathematically provable ideal of plain language should be rejected. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because reader diversity and weak measurement make one-size-fits-all optimization conceptually unsound. It connects to universal metrics, reader heterogeneity, plain language, measurement limits, contract drafting, consumer diversity.

a universal and mathematically provable ideal of plain language should be rejected. This matters because reader diversity and weak measurement make one-size-fits-all optimization conceptually unsound.

universal metrics
reader heterogeneity
plain language
measurement limits
contract drafting
consumer diversity

Source-draft page 4 (PDF page 4) develops this proposition in Introduction.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 4, that plain-language regulation can divert attention from substantive terms and market conditions to drafting compliance. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because a focus on form can consume regulatory resources while leaving unfair prices or power structures untouched. It connects to regulatory diversion, substantive terms, market structure, compliance costs, consumer harm, legal form.

plain-language regulation can divert attention from substantive terms and market conditions to drafting compliance. This matters because a focus on form can consume regulatory resources while leaving unfair prices or power structures untouched.

regulatory diversion
substantive terms
market structure
compliance costs
consumer harm
legal form


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 4, that plain-language policy can entrench a moralized duty to read and strict textualism. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because making forms nominally readable may strengthen doctrines that blame consumers for failing to absorb standardized terms. It connects to duty to read, textualism, consumer blame, shrinkwrap, contract doctrine, plain language.

plain-language policy can entrench a moralized duty to read and strict textualism. This matters because making forms nominally readable may strengthen doctrines that blame consumers for failing to absorb standardized terms.

duty to read
textualism
consumer blame
shrinkwrap
contract doctrine
plain language


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 4, that personalized smart readers offer a better direction than drafting for an imagined average consumer. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because technology can tailor language, presentation, and modality to actual linguistic and cognitive needs. It connects to smart readers, personalization, accessibility, consumer diversity, legal technology, contract comprehension.

personalized smart readers offer a better direction than drafting for an imagined average consumer. This matters because technology can tailor language, presentation, and modality to actual linguistic and cognitive needs.

smart readers
personalization
accessibility
consumer diversity
legal technology
contract comprehension


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 4, that the plain-language story illustrates how folk empiricism can drive well-intentioned legal reform. The discussion situates this proposition within Introduction and the article's empirical reassessment of the plain-language movement. This is significant because easy-to-repeat empirical myths can redirect law and scholarship away from interventions with stronger evidence. It connects to folk empiricism, legal myths, evidence-based policy, consumer reform, ALL-CAPS, scholarly rigor.

the plain-language story illustrates how folk empiricism can drive well-intentioned legal reform. This matters because easy-to-repeat empirical myths can redirect law and scholarship away from interventions with stronger evidence.

folk empiricism
legal myths
evidence-based policy
consumer reform
ALL-CAPS
scholarly rigor


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 5, that the plain-language movement combines political invisibility, elite endorsement, populist rhetoric, and global reach. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because its paradoxical coalition helps explain how a low-salience project achieved durable bipartisan influence. It connects to political coalition, populism, elite endorsement, global diffusion, plain language, regulatory history.

the plain-language movement combines political invisibility, elite endorsement, populist rhetoric, and global reach. This matters because its paradoxical coalition helps explain how a low-salience project achieved durable bipartisan influence.

political coalition
populism
elite endorsement
global diffusion
plain language
regulatory history

The critique targets the movement's empirical and universal claims, not the value of clear drafting in every context or for every reader.

Source-draft page 5 (PDF page 5) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 5, that the movement emerged when substantive consumer regulation weakened and process-based reform looked more attainable. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because historical context may have pushed advocates toward technocratic drafting rules instead of contested questions of value and power. It connects to regulatory history, FTC, process reform, substantive regulation, technocracy, consumer politics.

the movement emerged when substantive consumer regulation weakened and process-based reform looked more attainable. This matters because historical context may have pushed advocates toward technocratic drafting rules instead of contested questions of value and power.

regulatory history
FTC
process reform
substantive regulation
technocracy
consumer politics


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 5, that literacy anxiety and the Nation at Risk narrative helped shape the movement's view of Americans as deeply undereducated. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because a false or exaggerated diagnosis of schooling can become embedded in later disclosure policy. It connects to literacy anxiety, Nation at Risk, education myths, policy diffusion, adult readers, plain language.

literacy anxiety and the Nation at Risk narrative helped shape the movement's view of Americans as deeply undereducated. This matters because a false or exaggerated diagnosis of schooling can become embedded in later disclosure policy.

literacy anxiety
Nation at Risk
education myths
policy diffusion
adult readers
plain language


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 5, that the movement's manuals and style guides conceal jurisprudential and political commitments beneath craft advice. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because choices about legal form, access, and obligation cannot be reduced to preferences between short and long phrases. It connects to legal style, jurisprudence, political commitments, technocracy, legal realism, drafting manuals.

the movement's manuals and style guides conceal jurisprudential and political commitments beneath craft advice. This matters because choices about legal form, access, and obligation cannot be reduced to preferences between short and long phrases.

legal style
jurisprudence
political commitments
technocracy
legal realism
drafting manuals


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 6, that plain-language reform has attracted bipartisan political support and civil-rights rhetoric. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because broad rhetorical appeal helps explain statutory success without resolving whether the policies work. It connects to bipartisanship, Plain Writing Act, civil rights, political rhetoric, regulatory adoption, policy efficacy.

plain-language reform has attracted bipartisan political support and civil-rights rhetoric. This matters because broad rhetorical appeal helps explain statutory success without resolving whether the policies work.

bipartisanship
Plain Writing Act
civil rights
political rhetoric
regulatory adoption
policy efficacy

Source-draft page 6 (PDF page 6) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 6, that the claim that people do not read contracts at all is exaggerated. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because consumers sometimes read, ask about policies, use reputational information, and rely on market signals outside the formal document. It connects to consumer reading, reputation markets, warranties, information channels, empirical behavior, no-reading problem.

the claim that people do not read contracts at all is exaggerated. This matters because consumers sometimes read, ask about policies, use reputational information, and rely on market signals outside the formal document.

consumer reading
reputation markets
warranties
information channels
empirical behavior
no-reading problem


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 6, that even demonstrated nonreading of online terms does not establish that reading consistently improves consumer outcomes. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the welfare value of disclosure depends on actionability and decision effects, not exposure to text alone. It connects to online terms, disclosure efficacy, consumer decisions, welfare, attention, contract reading.

even demonstrated nonreading of online terms does not establish that reading consistently improves consumer outcomes. This matters because the welfare value of disclosure depends on actionability and decision effects, not exposure to text alone.

online terms
disclosure efficacy
consumer decisions
welfare
attention
contract reading


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 6, that legalese is only one of several plausible obstacles to reading. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because a nonnegotiable document may remain irrational to read even after excellent copyediting. It connects to legalese, nonnegotiability, rational inattention, reading costs, causal mechanisms, consumer contracts.

legalese is only one of several plausible obstacles to reading. This matters because a nonnegotiable document may remain irrational to read even after excellent copyediting.

legalese
nonnegotiability
rational inattention
reading costs
causal mechanisms
consumer contracts


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 7, that concerns about unreadable boilerplate have supported nonenforcement, enhanced disclosure, and substantive policing. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because an empirical claim about language supplies leverage for much broader doctrinal responses. It connects to boilerplate, nonenforcement, conspicuous disclosure, substantive regulation, consumer consent, doctrinal implications.

concerns about unreadable boilerplate have supported nonenforcement, enhanced disclosure, and substantive policing. This matters because an empirical claim about language supplies leverage for much broader doctrinal responses.

boilerplate
nonenforcement
conspicuous disclosure
substantive regulation
consumer consent
doctrinal implications

Source-draft page 7 (PDF page 7) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 7, that plain-language advocates generally characterize legalese as poor craft rather than strategic obfuscation. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the diagnosis places responsibility on drafting technique and promises reform without confronting distributional conflict. It connects to bad drafting, strategic obfuscation, lawyer incentives, craft, distributional conflict, plain language.

plain-language advocates generally characterize legalese as poor craft rather than strategic obfuscation. This matters because the diagnosis places responsibility on drafting technique and promises reform without confronting distributional conflict.

bad drafting
strategic obfuscation
lawyer incentives
craft
distributional conflict
plain language


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 7, that translation from technical legal language to plain language can be lossy. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because precision sometimes requires distinctions that a compressed or simplified version may erase. It connects to lossy compression, legal precision, technical terms, information theory, contract interpretation, simplification.

translation from technical legal language to plain language can be lossy. This matters because precision sometimes requires distinctions that a compressed or simplified version may erase.

lossy compression
legal precision
technical terms
information theory
contract interpretation
simplification


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 7, that the case for plain language cannot assume that simplicity and precision are costless complements. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the relevant policy question is how much meaning, detail, and length a redesign changes. It connects to simplicity, precision, tradeoffs, document design, legal meaning, consumer disclosure.

the case for plain language cannot assume that simplicity and precision are costless complements. This matters because the relevant policy question is how much meaning, detail, and length a redesign changes.

simplicity
precision
tradeoffs
document design
legal meaning
consumer disclosure


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 8, that explaining technical terms in simpler words often lengthens the document. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because improvement along sentence complexity can worsen the separate burden of reading time and volume. It connects to length-simplicity tradeoff, document burden, reading time, technical explanation, insurance contracts, multi-dimensional readability.

explaining technical terms in simpler words often lengthens the document. This matters because improvement along sentence complexity can worsen the separate burden of reading time and volume.

length-simplicity tradeoff
document burden
reading time
technical explanation
insurance contracts
multi-dimensional readability

Source-draft page 8 (PDF page 8) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 8, that Dutch insurance simplification produced documents that were easier by one metric but substantially longer. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because a policy can look successful on a chosen score while leaving total accessibility unchanged or worse. It connects to insurance policies, metric tradeoffs, document length, policy evaluation, readability, unintended effects.

Dutch insurance simplification produced documents that were easier by one metric but substantially longer. This matters because a policy can look successful on a chosen score while leaving total accessibility unchanged or worse.

insurance policies
metric tradeoffs
document length
policy evaluation
readability
unintended effects


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 8, that the relationship between linguistic simplification and comprehension is not linear. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because readers may prefer and understand text matched to their knowledge rather than the simplest possible prose. It connects to comprehension, reader-text match, nonlinearity, linguistic complexity, consumer preferences, plain language.

the relationship between linguistic simplification and comprehension is not linear. This matters because readers may prefer and understand text matched to their knowledge rather than the simplest possible prose.

comprehension
reader-text match
nonlinearity
linguistic complexity
consumer preferences
plain language


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 8, that readability formulas operationalize a rich construct through a few formal textual counts. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because word, sentence, character, syllable, and difficult-word features omit meaning, discourse, context, and reader characteristics. It connects to operationalization, formal metrics, linguistics, text features, construct validity, reader context.

readability formulas operationalize a rich construct through a few formal textual counts. This matters because word, sentence, character, syllable, and difficult-word features omit meaning, discourse, context, and reader characteristics.

operationalization
formal metrics
linguistics
text features
construct validity
reader context


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 9, that widely used readability tests rely on different combinations of surface features. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because scores labeled readability may encode materially different constructs and assumptions. It connects to Flesch-Kincaid, SMOG, Gunning Fog, Coleman-Liau, surface features, measurement theory.

widely used readability tests rely on different combinations of surface features. This matters because scores labeled readability may encode materially different constructs and assumptions.

Flesch-Kincaid
SMOG
Gunning Fog
Coleman-Liau
surface features
measurement theory

Source-draft page 9 (PDF page 9) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 9, that policymakers and commercial actors use readability scores despite advocates' own warnings that the measures are reductive. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because a hedged research tool becomes dangerous when converted into a compliance threshold. It connects to regulatory thresholds, measurement caution, compliance, readability software, policy implementation, Goodhart's law.

policymakers and commercial actors use readability scores despite advocates' own warnings that the measures are reductive. This matters because a hedged research tool becomes dangerous when converted into a compliance threshold.

regulatory thresholds
measurement caution
compliance
readability software
policy implementation
Goodhart's law


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 9, that readability measures require separate evaluation of internal and external validity. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because agreement among tests and correlation with real comprehension are distinct questions that policy must not conflate. It connects to internal validity, external validity, measurement theory, comprehension, reliability, policy evidence.

readability measures require separate evaluation of internal and external validity. This matters because agreement among tests and correlation with real comprehension are distinct questions that policy must not conflate.

internal validity
external validity
measurement theory
comprehension
reliability
policy evidence


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 10, that readability formulas that purport to measure one construct should correlate on a common text corpus. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because inter-test agreement is a basic prediction of a unified readability concept. It connects to correlation, construct validity, Gutenberg corpus, measurement consistency, readability formulas, empirical test.

readability formulas that purport to measure one construct should correlate on a common text corpus. This matters because inter-test agreement is a basic prediction of a unified readability concept.

correlation
construct validity
Gutenberg corpus
measurement consistency
readability formulas
empirical test

Source-draft page 10 (PDF page 10) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 10, that several leading readability tests correlate weakly or negatively with one another. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the label readability does not guarantee that different formulas measure the same property. It connects to correlation matrix, SMOG, Flesch-Kincaid, Coleman-Liau, construct fragmentation, metric validity.

several leading readability tests correlate weakly or negatively with one another. This matters because the label readability does not guarantee that different formulas measure the same property.

correlation matrix
SMOG
Flesch-Kincaid
Coleman-Liau
construct fragmentation
metric validity


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 10, that near-perfect correlations among some tests suggest redundancy rather than independent confirmation. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because multiple formula outputs may give a false appearance of triangulation while repeating the same signal. It connects to redundancy, triangulation, Dale-Chall, Gunning Fog, measurement independence, readability.

near-perfect correlations among some tests suggest redundancy rather than independent confirmation. This matters because multiple formula outputs may give a false appearance of triangulation while repeating the same signal.

redundancy
triangulation
Dale-Chall
Gunning Fog
measurement independence
readability


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 10, that different software libraries implementing Flesch-Kincaid differed by an average of 4.6 grade levels on the same contract paragraphs. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because implementation choice alone can determine legal compliance or scholarly conclusions. It connects to software implementation, Flesch-Kincaid, 4.6-year gap, reproducibility, metric gaming, contract paragraphs.

different software libraries implementing Flesch-Kincaid differed by an average of 4.6 grade levels on the same contract paragraphs. This matters because implementation choice alone can determine legal compliance or scholarly conclusions.

software implementation
Flesch-Kincaid
4.6-year gap
reproducibility
metric gaming
contract paragraphs


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 11, that sentence tokenization is ambiguous in contracts with headings, bullets, definitions, and conversion artifacts. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because a mechanical parser can transform formatting into fantastical grade-level scores unrelated to reader difficulty. It connects to sentence tokenization, document formatting, conversion artifacts, headings, measurement error, legal documents.

sentence tokenization is ambiguous in contracts with headings, bullets, definitions, and conversion artifacts. This matters because a mechanical parser can transform formatting into fantastical grade-level scores unrelated to reader difficulty.

sentence tokenization
document formatting
conversion artifacts
headings
measurement error
legal documents

Source-draft page 11 (PDF page 11) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 11, that word and syllable counts depend on contested linguistic and computational choices. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because compound words, contractions, accents, URLs, and pronunciation expose hidden discretion inside supposedly objective formulas. It connects to word boundaries, syllable counting, pronunciation, URLs, computational linguistics, objectivity.

word and syllable counts depend on contested linguistic and computational choices. This matters because compound words, contractions, accents, URLs, and pronunciation expose hidden discretion inside supposedly objective formulas.

word boundaries
syllable counting
pronunciation
URLs
computational linguistics
objectivity


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 11, that earlier readability research often overlooks implementation sensitivity. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because replication and regulatory use require disclosure of software, preprocessing, and tokenization choices. It connects to method transparency, replication, preprocessing, software libraries, readability research, measurement sensitivity.

earlier readability research often overlooks implementation sensitivity. This matters because replication and regulatory use require disclosure of software, preprocessing, and tokenization choices.

method transparency
replication
preprocessing
software libraries
readability research
measurement sensitivity


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 11, that readability formulas show mixed and sometimes inverse relationships with actual comprehension. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because formal ease cannot be assumed to improve learning, preference, or use without outcome validation. It connects to external validity, comprehension, reader preference, scientific abstracts, outcome validation, readability.

readability formulas show mixed and sometimes inverse relationships with actual comprehension. This matters because formal ease cannot be assumed to improve learning, preference, or use without outcome validation.

external validity
comprehension
reader preference
scientific abstracts
outcome validation
readability


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 12, that classic formulas explain only a small share of measured comprehension and reading-speed variance in recent studies. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because modest correlations are insufficient to support precise grade-level mandates. It connects to explained variance, reading speed, comprehension, Wikipedia, grade mandates, predictive validity.

classic formulas explain only a small share of measured comprehension and reading-speed variance in recent studies. This matters because modest correlations are insufficient to support precise grade-level mandates.

explained variance
reading speed
comprehension
Wikipedia
grade mandates
predictive validity

Source-draft page 12 (PDF page 12) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 12, that contract-simplification experiments produce mixed or null effects on understanding, trust, and behavior. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because copyediting cannot be presumed to change consumer choices or dispute responses. It connects to contract experiments, simplification, trust, consumer behavior, comprehension, causal evidence.

contract-simplification experiments produce mixed or null effects on understanding, trust, and behavior. This matters because copyediting cannot be presumed to change consumer choices or dispute responses.

contract experiments
simplification
trust
consumer behavior
comprehension
causal evidence


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 12, that some experiments found the nominally least readable forms produced the most correct answers. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because formula scores may diverge from task-specific comprehension in legally meaningful settings. It connects to correct answers, contract forms, formula validity, task-specific comprehension, counterintuitive evidence, consumer research.

some experiments found the nominally least readable forms produced the most correct answers. This matters because formula scores may diverge from task-specific comprehension in legally meaningful settings.

correct answers
contract forms
formula validity
task-specific comprehension
counterintuitive evidence
consumer research


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 12, that making contracts shorter may improve comprehension more reliably than changing formula scores. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because document volume is a distinct access problem that sentence-level metrics often miss. It connects to contract length, shortening, comprehension, document design, reading burden, multi-dimensional access.

making contracts shorter may improve comprehension more reliably than changing formula scores. This matters because document volume is a distinct access problem that sentence-level metrics often miss.

contract length
shortening
comprehension
document design
reading burden
multi-dimensional access


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 12, that a large experiment found easier contracts felt more understandable but did not change advice-seeking, complaints, or legal action. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because subjective clarity and consequential behavior should be measured separately. It connects to subjective understanding, behavioral outcomes, legal action, advice seeking, insurance contracts, experimental evidence.

a large experiment found easier contracts felt more understandable but did not change advice-seeking, complaints, or legal action. This matters because subjective clarity and consequential behavior should be measured separately.

subjective understanding
behavioral outcomes
legal action
advice seeking
insurance contracts
experimental evidence


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 13, that claims that half of American adults cannot read past eighth grade are pervasive across scholarship and government materials. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because repetition across institutions can create authority without a sound originating source. It connects to citation cascade, adult literacy, government reports, legal scholarship, eighth-grade claim, epistemic reliability.

claims that half of American adults cannot read past eighth grade are pervasive across scholarship and government materials. This matters because repetition across institutions can create authority without a sound originating source.

citation cascade
adult literacy
government reports
legal scholarship
eighth-grade claim
epistemic reliability

Source-draft page 13 (PDF page 13) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 13, that if most adults truly read like young teenagers, the implication would reach democracy and innovation far beyond contracts. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the extraordinary breadth of the claim should trigger correspondingly rigorous validation. It connects to extraordinary claims, democracy, innovation, literacy, policy stakes, evidence standards.

if most adults truly read like young teenagers, the implication would reach democracy and innovation far beyond contracts. This matters because the extraordinary breadth of the claim should trigger correspondingly rigorous validation.

extraordinary claims
democracy
innovation
literacy
policy stakes
evidence standards


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 13, that Census educational-attainment data contradicts the premise of population-wide arrest at middle-school reading levels. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because formal attainment cannot prove comprehension but supplies a powerful plausibility check against the myth. It connects to Census, educational attainment, plausibility check, adult readers, literacy myth, population data.

Census educational-attainment data contradicts the premise of population-wide arrest at middle-school reading levels. This matters because formal attainment cannot prove comprehension but supplies a powerful plausibility check against the myth.

Census
educational attainment
plausibility check
adult readers
literacy myth
population data


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 13, that adult literacy should not be inferred by mechanically equating formula grade levels with completed schooling. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the two scales describe different constructs and their numerical resemblance invites category error. It connects to grade-level formulas, schooling, construct mismatch, adult literacy, measurement interpretation, category error.

adult literacy should not be inferred by mechanically equating formula grade levels with completed schooling. This matters because the two scales describe different constructs and their numerical resemblance invites category error.

grade-level formulas
schooling
construct mismatch
adult literacy
measurement interpretation
category error


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 14, that PIAAC finds most American adults have mid or high English proficiency. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because direct skill assessment does not support describing a majority as functionally or partially illiterate. It connects to PIAAC, adult proficiency, direct assessment, functional literacy, international comparison, education evidence.

PIAAC finds most American adults have mid or high English proficiency. This matters because direct skill assessment does not support describing a majority as functionally or partially illiterate.

PIAAC
adult proficiency
direct assessment
functional literacy
international comparison
education evidence

Source-draft page 14 (PDF page 14) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 14, that NAAL expressly warns that grade-level translations of literacy are arbitrary. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because sources invoked for the eighth-grade claim undermine rather than substantiate that translation. It connects to NAAL, arbitrary standards, source verification, literacy scales, citation accuracy, grade equivalents.

NAAL expressly warns that grade-level translations of literacy are arbitrary. This matters because sources invoked for the eighth-grade claim undermine rather than substantiate that translation.

NAAL
arbitrary standards
source verification
literacy scales
citation accuracy
grade equivalents


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 14, that people classified below a demanding literacy threshold can still perform complex document tasks. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because categorical labels like partially illiterate obscure functional capacities and threshold choices. It connects to literacy thresholds, functional capacity, document comparison, classification, public rhetoric, measurement.

people classified below a demanding literacy threshold can still perform complex document tasks. This matters because categorical labels like partially illiterate obscure functional capacities and threshold choices.

literacy thresholds
functional capacity
document comparison
classification
public rhetoric
measurement


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 14, that a Gallup report created an alarming partial-illiteracy figure by defining everyone below Level 3 as partially illiterate. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the headline depends on a stipulative label not used by the underlying international assessment. It connects to Gallup, PIAAC levels, definitional inflation, partial illiteracy, source interpretation, public statistics.

a Gallup report created an alarming partial-illiteracy figure by defining everyone below Level 3 as partially illiterate. This matters because the headline depends on a stipulative label not used by the underlying international assessment.

Gallup
PIAAC levels
definitional inflation
partial illiteracy
source interpretation
public statistics


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 14, that self-reported and educational evidence should be read alongside direct assessments rather than collapsed into a single grade number. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because triangulation reveals reader capacity more faithfully than one unsupported conversion. It connects to triangulation, self-report, education data, direct assessment, literacy, measurement pluralism.

self-reported and educational evidence should be read alongside direct assessments rather than collapsed into a single grade number. This matters because triangulation reveals reader capacity more faithfully than one unsupported conversion.

triangulation
self-report
education data
direct assessment
literacy
measurement pluralism


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 15, that health-literacy studies arise in contexts of stress, disability, and specialized medical material. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because findings from small clinical samples should not be generalized uncritically to all adults and contracts. It connects to health literacy, external validity, clinical context, sample size, medical disclosures, generalization.

health-literacy studies arise in contexts of stress, disability, and specialized medical material. This matters because findings from small clinical samples should not be generalized uncritically to all adults and contracts.

health literacy
external validity
clinical context
sample size
medical disclosures
generalization

Source-draft page 15 (PDF page 15) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 15, that a central grade-equivalence assertion traces to an unsourced rough approximation in an old textbook. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because citation chains can transform a tentative teaching heuristic into an apparent empirical fact. It connects to citation chain, rough approximation, textbook, grade equivalence, legal myths, source audit.

a central grade-equivalence assertion traces to an unsourced rough approximation in an old textbook. This matters because citation chains can transform a tentative teaching heuristic into an apparent empirical fact.

citation chain
rough approximation
textbook
grade equivalence
legal myths
source audit


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 15, that book, news, and social-media consumption provide a sanity check against majority illiteracy. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because observed voluntary interaction with nontrivial text is inconsistent with the movement's most sweeping premise. It connects to reading habits, book consumption, news, social media, sanity check, adult literacy.

book, news, and social-media consumption provide a sanity check against majority illiteracy. This matters because observed voluntary interaction with nontrivial text is inconsistent with the movement's most sweeping premise.

reading habits
book consumption
news
social media
sanity check
adult literacy


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 15, that most eighth and twelfth graders read at or above their own basic grade level. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because the claim that half of adults never progress beyond eighth grade is implausible even against school-age benchmarks. It connects to student achievement, eighth graders, twelfth graders, basic reading, adult literacy, plausibility.

most eighth and twelfth graders read at or above their own basic grade level. This matters because the claim that half of adults never progress beyond eighth grade is implausible even against school-age benchmarks.

student achievement
eighth graders
twelfth graders
basic reading
adult literacy
plausibility


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 16, that policy should focus on people with real literacy and accessibility vulnerabilities rather than an invented average deficit. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because rejecting a population-wide myth strengthens rather than weakens the case for targeted support. It connects to targeted policy, literacy vulnerability, accessibility, consumer protection, average consumer, distributional focus.

policy should focus on people with real literacy and accessibility vulnerabilities rather than an invented average deficit. This matters because rejecting a population-wide myth strengthens rather than weakens the case for targeted support.

targeted policy
literacy vulnerability
accessibility
consumer protection
average consumer
distributional focus

Source-draft page 16 (PDF page 16) develops this proposition in I. The Four Tenets of Plain Language.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 16, that readers who struggle with contracts are heterogeneous in language, cognition, vision, education, age, and context. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because a single grade-level score cannot specify the intervention that each person needs. It connects to reader heterogeneity, neurodivergence, language access, visual accessibility, context, personalization.

readers who struggle with contracts are heterogeneous in language, cognition, vision, education, age, and context. This matters because a single grade-level score cannot specify the intervention that each person needs.

reader heterogeneity
neurodivergence
language access
visual accessibility
context
personalization


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 16, that false assumptions about the average reader produce false expectations and misdirected policy. The discussion situates this proposition within I. The Four Tenets of Plain Language and the article's empirical reassessment of the plain-language movement. This is significant because scientific rigor matters because measurement errors become embedded in broad legal mandates. It connects to average reader, policy design, scientific rigor, measurement error, legal mandates, plain language.

false assumptions about the average reader produce false expectations and misdirected policy. This matters because scientific rigor matters because measurement errors become embedded in broad legal mandates.

average reader
policy design
scientific rigor
measurement error
legal mandates
plain language


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 17, that the study tests the movement's central readability claim despite serious doubts about traditional metrics. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because robust preprocessing, aggregation, and benchmarking allow a conservative test using the movement's own tools. It connects to adversarial testing, robust metrics, benchmarking, plain-language claims, empirical design, readability.

the study tests the movement's central readability claim despite serious doubts about traditional metrics. This matters because robust preprocessing, aggregation, and benchmarking allow a conservative test using the movement's own tools.

adversarial testing
robust metrics
benchmarking
plain-language claims
empirical design
readability

The dataset is exceptionally large but not exhaustive, and classification performance is lower on a blind audit than on held-out examples from the labeled categories.

Source-draft page 17 (PDF page 17) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 17, that SEC exhibit-10 filings provide a corpus of roughly 1.2 million material commercial contracts. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because mandatory disclosure creates longitudinal legal data at a scale unavailable through conventional manual collection. It connects to SEC EDGAR, Exhibit 10, material contracts, mandatory disclosure, commercial agreements, big data.

SEC exhibit-10 filings provide a corpus of roughly 1.2 million material commercial contracts. This matters because mandatory disclosure creates longitudinal legal data at a scale unavailable through conventional manual collection.

SEC EDGAR
Exhibit 10
material contracts
mandatory disclosure
commercial agreements
big data


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 17, that format normalization is essential because parsing artifacts can distort readability scores. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because measurement validity depends on cleaning HTML, PDF, and plain-text sources into comparable documents. It connects to data cleaning, PDF conversion, HTML parsing, format artifacts, readability measurement, reproducibility.

format normalization is essential because parsing artifacts can distort readability scores. This matters because measurement validity depends on cleaning HTML, PDF, and plain-text sources into comparable documents.

data cleaning
PDF conversion
HTML parsing
format artifacts
readability measurement
reproducibility


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 17, that manual classification is infeasible for a million-contract corpus. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because legal NLP is necessary to separate heterogeneous agreement types before meaningful comparison. It connects to document classification, legal NLP, scale, contract taxonomy, machine learning, empirical law.

manual classification is infeasible for a million-contract corpus. This matters because legal NLP is necessary to separate heterogeneous agreement types before meaningful comparison.

document classification
legal NLP
scale
contract taxonomy
machine learning
empirical law


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 18, that hundreds of hand-coded title rules classified only about one quarter of the contracts. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because legal drafting variation and idiosyncratic labels limit brittle rule-based systems. It connects to rule-based classification, contract titles, drafting variation, coverage, legal language, automation.

hundreds of hand-coded title rules classified only about one quarter of the contracts. This matters because legal drafting variation and idiosyncratic labels limit brittle rule-based systems.

rule-based classification
contract titles
drafting variation
coverage
legal language
automation

Source-draft page 18 (PDF page 18) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 18, that contract titles can supply weak labels created by the drafting lawyers themselves. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because built-in metadata avoids prohibitively expensive manual annotation while retaining legally informed categories. It connects to weak supervision, built-in labels, contract titles, annotation cost, legal expertise, training data.

contract titles can supply weak labels created by the drafting lawyers themselves. This matters because built-in metadata avoids prohibitively expensive manual annotation while retaining legally informed categories.

weak supervision
built-in labels
contract titles
annotation cost
legal expertise
training data


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 18, that titles were grouped into ten categories and removed before model training. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because scrubbing the label forces the classifier to learn substantive content rather than a superficial title cue. It connects to label leakage, title scrubbing, contract categories, substantive content, machine learning, model design.

titles were grouped into ten categories and removed before model training. This matters because scrubbing the label forces the classifier to learn substantive content rather than a superficial title cue.

label leakage
title scrubbing
contract categories
substantive content
machine learning
model design


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 18, that the weak-label process produced 275,480 training examples across leading contract categories. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because large labeled data makes high-coverage classification possible without pretending the categories exhaust every agreement type. It connects to training corpus, contract taxonomy, weak labels, sample size, classification coverage, legal datasets.

the weak-label process produced 275,480 training examples across leading contract categories. This matters because large labeled data makes high-coverage classification possible without pretending the categories exhaust every agreement type.

training corpus
contract taxonomy
weak labels
sample size
classification coverage
legal datasets


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 19, that traditional TF-IDF vectors outperformed transformer embeddings for contract-type classification. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because newer and more computationally intensive representations are not automatically superior for domain tasks driven by distinctive vocabulary. It connects to TF-IDF, transformers, BERT, legal BERT, model comparison, domain vocabulary.

traditional TF-IDF vectors outperformed transformer embeddings for contract-type classification. This matters because newer and more computationally intensive representations are not automatically superior for domain tasks driven by distinctive vocabulary.

TF-IDF
transformers
BERT
legal BERT
model comparison
domain vocabulary

Source-draft page 19 (PDF page 19) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 19, that TF-IDF works well because rare specialized words distinguish contract categories. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because terms such as mortgage, salary, collateral, and termination carry more class information than ubiquitous words. It connects to feature importance, specialized vocabulary, document vectors, contract types, information retrieval, TF-IDF.

TF-IDF works well because rare specialized words distinguish contract categories. This matters because terms such as mortgage, salary, collateral, and termination carry more class information than ubiquitous words.

feature importance
specialized vocabulary
document vectors
contract types
information retrieval
TF-IDF


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 19, that LightGBM supplied the best combination of accuracy and inference speed among tested classifiers. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because large-corpus analysis requires attention to computational cost as well as headline accuracy. It connects to LightGBM, gradient boosting, inference time, classification accuracy, computational efficiency, model selection.

LightGBM supplied the best combination of accuracy and inference speed among tested classifiers. This matters because large-corpus analysis requires attention to computational cost as well as headline accuracy.

LightGBM
gradient boosting
inference time
classification accuracy
computational efficiency
model selection


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 19, that complex models and embeddings imposed substantial computing costs without winning the task. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because empirical legal research benefits from testing simple baselines before adopting resource-intensive architectures. It connects to simple baselines, GPU computing, model efficiency, legal NLP, research design, cost-benefit analysis.

complex models and embeddings imposed substantial computing costs without winning the task. This matters because empirical legal research benefits from testing simple baselines before adopting resource-intensive architectures.

simple baselines
GPU computing
model efficiency
legal NLP
research design
cost-benefit analysis


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 20, that the classifier reached 96.67 percent balanced accuracy within the defined training categories. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because high held-out performance supports large-scale descriptive use when its domain boundary is respected. It connects to balanced accuracy, held-out test, contract classification, model performance, domain boundary, legal NLP.

the classifier reached 96.67 percent balanced accuracy within the defined training categories. This matters because high held-out performance supports large-scale descriptive use when its domain boundary is respected.

balanced accuracy
held-out test
contract classification
model performance
domain boundary
legal NLP

Source-draft page 20 (PDF page 20) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 20, that category-level F1 scores ranged from roughly 93 to 100 percent for some agreement types. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because aggregate accuracy should be supplemented with class-specific performance to detect uneven errors. It connects to F1 score, class imbalance, category performance, evaluation metrics, contract taxonomy, model audit.

category-level F1 scores ranged from roughly 93 to 100 percent for some agreement types. This matters because aggregate accuracy should be supplemented with class-specific performance to detect uneven errors.

F1 score
class imbalance
category performance
evaluation metrics
contract taxonomy
model audit


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 20, that misclassification between credit and purchase agreements is substantively intelligible. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because error analysis can reveal overlapping legal content rather than random model failure. It connects to confusion matrix, credit agreements, purchase agreements, error analysis, legal overlap, model interpretation.

misclassification between credit and purchase agreements is substantively intelligible. This matters because error analysis can reveal overlapping legal content rather than random model failure.

confusion matrix
credit agreements
purchase agreements
error analysis
legal overlap
model interpretation


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 20, that a blind audit outside the structured test set produced a lower 78 percent balanced accuracy. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because real-world generalization is materially weaker than performance on familiar categories and should bound downstream claims. It connects to blind audit, generalization, distribution shift, 78 percent, model validation, epistemic caution.

a blind audit outside the structured test set produced a lower 78 percent balanced accuracy. This matters because real-world generalization is materially weaker than performance on familiar categories and should bound downstream claims.

blind audit
generalization
distribution shift
78 percent
model validation
epistemic caution


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 21, that the CARD Act created a near-marketwide database of consumer credit-card agreements. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because regulatory filing duties can generate unusually representative data about standardized consumer contracts. It connects to CARD Act, CFPB, credit-card agreements, regulatory data, consumer contracts, market coverage.

the CARD Act created a near-marketwide database of consumer credit-card agreements. This matters because regulatory filing duties can generate unusually representative data about standardized consumer contracts.

CARD Act
CFPB
credit-card agreements
regulatory data
consumer contracts
market coverage

Source-draft page 21 (PDF page 21) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 21, that credit-card contracts matter at population scale because most Americans use multiple cards. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because a standardized form in the dataset can govern millions of individual transactions. It connects to credit cards, population exposure, standard forms, consumer finance, contract prevalence, representativeness.

credit-card contracts matter at population scale because most Americans use multiple cards. This matters because a standardized form in the dataset can govern millions of individual transactions.

credit cards
population exposure
standard forms
consumer finance
contract prevalence
representativeness


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 21, that the CFPB files required extensive repair because of missing mappings, duplicates, scans, and corrupt documents. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because public availability does not eliminate data-engineering and quality-assurance burdens. It connects to data quality, CFPB, PDF repair, duplicates, scanned documents, quality assurance.

the CFPB files required extensive repair because of missing mappings, duplicates, scans, and corrupt documents. This matters because public availability does not eliminate data-engineering and quality-assurance burdens.

data quality
CFPB
PDF repair
duplicates
scanned documents
quality assurance


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 21, that quality control yielded 93,078 readable credit agreements from 95,071 deduplicated files. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because transparent attrition documents the difference between nominal archives and analyzable data. It connects to sample attrition, credit agreements, deduplication, processable files, data pipeline, transparency.

quality control yielded 93,078 readable credit agreements from 95,071 deduplicated files. This matters because transparent attrition documents the difference between nominal archives and analyzable data.

sample attrition
credit agreements
deduplication
processable files
data pipeline
transparency


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 22, that the privacy-policy corpus uses historical web snapshots for more than half a million sites. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because archived longitudinal data permits comparison of drafting trends outside conventional filed contracts. It connects to privacy policies, Wayback Machine, Alexa rankings, longitudinal web data, consumer documents, data curation.

the privacy-policy corpus uses historical web snapshots for more than half a million sites. This matters because archived longitudinal data permits comparison of drafting trends outside conventional filed contracts.

privacy policies
Wayback Machine
Alexa rankings
longitudinal web data
consumer documents
data curation

Source-draft page 22 (PDF page 22) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 22, that missing-year privacy policies were filled forward under an assumption of no interim change. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because longitudinal scale requires an explicit imputation rule that may smooth real variation. It connects to imputation, missing data, privacy policies, longitudinal analysis, assumptions, measurement limits.

missing-year privacy policies were filled forward under an assumption of no interim change. This matters because longitudinal scale requires an explicit imputation rule that may smooth real variation.

imputation
missing data
privacy policies
longitudinal analysis
assumptions
measurement limits


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 22, that franchise-disclosure registries supplied 12,602 documents across multiple states. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because a hybrid consumer-commercial setting can be studied through decentralized public filing systems. It connects to franchise disclosure, state registries, FDD, hybrid contracts, data scraping, consumer business boundary.

franchise-disclosure registries supplied 12,602 documents across multiple states. This matters because a hybrid consumer-commercial setting can be studied through decentralized public filing systems.

franchise disclosure
state registries
FDD
hybrid contracts
data scraping
consumer business boundary


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 22, that franchise disclosures are legally mandated because prospective franchisees face information and power concerns. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because their readability has special policy relevance despite the nominal business status of the reader. It connects to franchisees, mandatory disclosure, information asymmetry, small business, consumer protection, readability.

franchise disclosures are legally mandated because prospective franchisees face information and power concerns. This matters because their readability has special policy relevance despite the nominal business status of the reader.

franchisees
mandatory disclosure
information asymmetry
small business
consumer protection
readability


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 23, that the final corpus contains 1,935,680 contracts, including 1.2 million commercial and 735,680 consumer or hybrid agreements. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because the study's claims rest on an unusually broad empirical base with multiple market settings. It connects to corpus size, commercial contracts, consumer agreements, hybrid agreements, empirical scale, contract research.

the final corpus contains 1,935,680 contracts, including 1.2 million commercial and 735,680 consumer or hybrid agreements. This matters because the study's claims rest on an unusually broad empirical base with multiple market settings.

corpus size
commercial contracts
consumer agreements
hybrid agreements
empirical scale
contract research

Source-draft page 23 (PDF page 23) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 23, that counts of standardized forms understate the number of transactions and people governed. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because one credit-card form can represent tens of thousands of legally distinct agreements. It connects to standardized forms, transaction multiplicity, unit of analysis, credit agreements, population coverage, representativeness.

counts of standardized forms understate the number of transactions and people governed. This matters because one credit-card form can represent tens of thousands of legally distinct agreements.

standardized forms
transaction multiplicity
unit of analysis
credit agreements
population coverage
representativeness


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 23, that the consumer sample overrepresents contracts already subject to disclosure and clarity regulation. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because regulatory selection may partly explain why observed credit agreements resemble ordinary news in difficulty. It connects to sample selection, regulated contracts, TILA, clarity mandates, credit cards, external validity.

the consumer sample overrepresents contracts already subject to disclosure and clarity regulation. This matters because regulatory selection may partly explain why observed credit agreements resemble ordinary news in difficulty.

sample selection
regulated contracts
TILA
clarity mandates
credit cards
external validity


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 23, that social-media terms, retail purchases, and consumer-to-consumer agreements are not adequately represented. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because the results should not be generalized to every consumer contract category. It connects to missing contract types, social media, retail contracts, consumer-to-consumer, sample scope, generalization.

social-media terms, retail purchases, and consumer-to-consumer agreements are not adequately represented. This matters because the results should not be generalized to every consumer contract category.

missing contract types
social media
retail contracts
consumer-to-consumer
sample scope
generalization


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 23, that the study's limitations remain modest relative to the tiny selected samples used in prior plain-language research. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because empirical criticism should compare realistic evidence bases rather than demand perfection only from the revisionist study. It connects to comparative evidence, sample size, prior literature, research limitations, burden of proof, empirical rigor.

the study's limitations remain modest relative to the tiny selected samples used in prior plain-language research. This matters because empirical criticism should compare realistic evidence bases rather than demand perfection only from the revisionist study.

comparative evidence
sample size
prior literature
research limitations
burden of proof
empirical rigor


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 24, that rigorous preprocessing removes nontextual artifacts and uses two sentence tokenizers. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because readability estimates are highly sensitive to boundaries, so redundant parsing reduces catastrophic scoring errors. It connects to preprocessing, sentence tokenizers, spaCy, TextBlob, robustness, measurement error.

rigorous preprocessing removes nontextual artifacts and uses two sentence tokenizers. This matters because readability estimates are highly sensitive to boundaries, so redundant parsing reduces catastrophic scoring errors.

preprocessing
sentence tokenizers
spaCy
TextBlob
robustness
measurement error

Source-draft page 24 (PDF page 24) develops this proposition in II. Data and Machine-Learning Classification.


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 24, that averaging multiple implementations of each formula constrains selective software choice. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because within-test aggregation reduces researcher degrees of freedom and opportunistic compliance gaming. It connects to ensemble measurement, software implementations, researcher discretion, metric gaming, robustness, readability tests.

averaging multiple implementations of each formula constrains selective software choice. This matters because within-test aggregation reduces researcher degrees of freedom and opportunistic compliance gaming.

ensemble measurement
software implementations
researcher discretion
metric gaming
robustness
readability tests


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 24, that the Composite Readability Measure averages across tests as well as across implementations. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because a summary measure can capture multiple formal dimensions while exposing that no single formula is privileged. It connects to Composite Readability Measure, averaging, measurement ensemble, readability dimensions, robustness, metric design.

the Composite Readability Measure averages across tests as well as across implementations. This matters because a summary measure can capture multiple formal dimensions while exposing that no single formula is privileged.

Composite Readability Measure
averaging
measurement ensemble
readability dimensions
robustness
metric design


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 24, that extreme and negative grade estimates were bounded to reduce the effect of pathological outputs. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because robust analysis must prevent parser failures from dominating large-sample comparisons. It connects to outlier handling, score capping, negative grades, robust statistics, parser error, data cleaning.

extreme and negative grade estimates were bounded to reduce the effect of pathological outputs. This matters because robust analysis must prevent parser failures from dominating large-sample comparisons.

outlier handling
score capping
negative grades
robust statistics
parser error
data cleaning


Professor Yonathan Arbel claims, in the article “The Readability of Contracts: Big Data Analysis” on page 25, that the benchmark corpus contains roughly 1.8 million news articles, reviews, debates, books, and reference texts. The discussion situates this proposition within II. Data and Machine-Learning Classification and the article's empirical reassessment of the plain-language movement. This is significant because comparison across familiar genres anchors formula outputs in observed reading behavior. It connects to benchmark corpus, news, Amazon reviews, presidential debates, books, Wikipedia.

the benchmark corpus contains roughly 1.8 million news articles, reviews, debates, books, and reference texts. This matters because comparison across familiar genres anchors formula outputs in observed reading behavior.

benchmark corpus
news
Amazon reviews
presidential debates
books
Wikipedia

Source-draft page 25 (PDF page 25) develops this proposition in II. Data and Machine-Learning Classification.