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Record W2107554981 · doi:10.1353/lan.2006.0011

Number Agreement in British and American English: Disagreeing to Agree Collectively

2006· article· en· W2107554981 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsMcMaster University
FundersUniversity of PennsylvaniaNational Institutes of HealthMax-Planck-GesellschaftNational Science Foundation
KeywordsNotional amountLinguisticsNounAgreementVerbSentencePredicative expressionPronounRepresentation (politics)PsychologyComputer scienceEconomicsPhilosophyPolitical science

Abstract

fetched live from OpenAlex

British and American speakers exhibit different verb number agreement patterns when sentence subjects have collective head nouns. From linguistic and psycholinguistic accounts of how agreement is implemented, three alternative hypotheses can be derived to explain these differences. The hypotheses involve variations in the representation of notional number, disparities in how notional and grammatical number are used, and inequalities in the grammatical number specifications of collective nouns. We carried out a series of corpus analyses, production experiments, and norming studies to test these hypotheses. The results converge to suggest that British and American speakers are equally sensitive to variations in notional number and implement subject-verb agreement in much the same way, but are likely to differ in the lexical specifications of number for collectives. The findings support a psycholinguistic theory that explains verb and pronoun agreement within a parallel architecture of lexical and syntactic formulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.224
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it