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Record W4249917842 · doi:10.1162/ling_a_00386

Feature Gluttony

2020· article· en· W4249917842 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

VenueLinguistic Inquiry · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsMcGill University
Fundersnot available
KeywordsFeature (linguistics)Nominative caseComputer scienceCliticLinguisticsConstraint (computer-aided design)Focus (optics)Distinctive featurePredicative expressionHierarchyNatural language processingArtificial intelligenceMathematicsVerbPhilosophyPhysics

Abstract

fetched live from OpenAlex

This article develops a new approach to a family of hierarchy-effect-inducing configurations, with a focus on Person Case Constraint effects, dative-nominative configurations, and copula constructions. The main line of approach in the recent literature is to attribute these effects to failures of ϕ-Agree or, more specifically, failures of nominal licensing or case checking. We propose that the problem in these configurations is unrelated to nominal licensing, but is instead the result of a probe participating in more than one Agree dependency, a configuration we refer to as feature gluttony. Feature gluttony does not in and of itself lead to ungrammaticality; rather, it can create irresolvably conflicting requirements for subsequent operations. We argue that in the case of clitic configurations, a probe that agrees with more than one DP creates an intervention problem for clitic doubling. In violations involving morphological agreement, gluttony in features may result in a configuration with no available morphological output.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
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)0.0010.001

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 head0.059
GPT teacher head0.266
Teacher spread0.207 · 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