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Bound Variable Anaphora

2017· other· en· W3169639225 on OpenAlexaff
Rose‐Marie Déchaine, Martina Wiltschko

Bibliographic record

VenueThe Wiley Blackwell Companion to Syntax, Second Edition · 2017
Typeother
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPronounAntecedent (behavioral psychology)SentenceLinguisticsSyntaxAnaphora (linguistics)Interpretation (philosophy)Computer scienceVariable (mathematics)Ellipsis (linguistics)MathematicsPhilosophyArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Abstract Bound variable anaphora (BVA) is the term given to contexts where a pronominal anaphor functions like a logical variable in that its interpretation co‐varies with the value assigned to its antecedent in a given universe of discourse. For example, in a math class with four girls (Alice, Beth, Carol, and Diane) the interpretation of the pronoun she in the English sentence Every girl in math hopes that she will be an astronaut varies according to which girl is picked out. For such a sentence to be evaluated as true it must be the case that each substitution of the pronoun for a constant yields a true proposition: Alice hopes that Alice will be an astronaut, Beth hopes that Beth will be an astronaut, and so on. Such sentences can be rendered by logical formulas such as [all x , λ x [girl‐in‐math( x )] λ x [ x hopes that x will be an astronaut]. The study of BVA has figured prominently in modern studies of grammar, and has proven to be an important testing ground for formal theories of syntax and semantics. In this chapter, we focus on the syntax of BVA. After establishing that the necessary and sufficient conditions for BVA represent a convergence of semantic and syntactic properties (section 1), we examine the distribution of bound variables in A‐binding and A′‐binding contexts (section 2). We then turn to the question of the form of (A‐bound and A′‐bound) BVAs (section 3), focusing on whether they can surface as reflexives, (overt or covert) pronouns, copy‐anaphors, unspecified bindable expressions (UBEs), or indexicals. After considering whether the internal syntax of bound variables is uniform (section 4), we attend to their semantic type (section 5). We conclude with a retrospective assessment of how analyses of BVA have developed over time, and speculate about future prospects (section 6).

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How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0900.007

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 head0.024
GPT teacher head0.240
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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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