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Record W3020931036 · doi:10.1111/synt.12195

Locality Domains in Syntax: Evidence from Sentence Processing

2020· article· en· W3020931036 on OpenAlexfundno aff
Stefan Keine

Bibliographic record

VenueSyntax · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsnot available
FundersSimon Fraser UniversityUniversity of Southern California
KeywordsParsingComputer scienceSyntaxLocalitySentenceGenerative grammarContext (archaeology)Sentence processingNatural language processingSequence (biology)Enhanced Data Rates for GSM EvolutionArtificial intelligenceSpeech recognitionLinguisticsGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract One of the main discoveries of generative syntax is that long‐distance extraction proceeds in a successive‐cyclic manner, in that these dependencies are comprised of a sequence of local extraction steps. This article provides support for this general picture by presenting novel parsing evidence for intermediate landing sites created by successive‐cyclic movement, and it uses this parsing evidence to investigate the distribution of intermediate gaps. The central findings of this article are that (i) there is evidence that successive‐cyclic movement targets the edge of CPs and that (ii) there is no comparable evidence for an intermediate landing site at vP edges. These findings are fully consistent with the classical view of successive cyclicity, according to which only finite‐clause edges host intermediate landing sites. In the context of phase theory, these results receive a straightforward explanation if CPs are phases but vPs are not. The processing evidence presented here thus provides a novel diagnostic for the distribution of phases and new evidence for their active role in online sentence processing.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.063
GPT teacher head0.267
Teacher spread0.204 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations20
Published2020
Admission routes1
Has abstractyes

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