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Record W2886666472 · doi:10.1163/23526416-00402002

The Cognitive Structure of Full-Verb Inversion and Existential Structures in English

2018· article· en· W2886666472 on OpenAlexaff
Patrick Duffley

Bibliographic record

VenueCognitive Semantics · 2018
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCognitive grammarVerbLinguisticsPredicate (mathematical logic)Construction grammarTransitive relationNegationInversion (geology)GrammarMathematicsWord orderCognitionComputer sciencePsychologyPhilosophyCombinatorics

Abstract

fetched live from OpenAlex

The goal of this study is to build on the Cognitive Grammar analysis of full-verb inversion ( FVI ) and existential structures proposed by Chen (2003, 2011 and 2013). Close attention will be given to two characteristics of these constructions not discussed by this author – lack of subject-verb agreement and the type of pronominal forms that occur in them – and their consequences for FVI ’s cognitive structure will be worked out. Further parallels between FVI and the existential there -construction will be brought to light concerning the type of verbal predicate allowed, negation, transitivity, agreement patterns, presentational function, pronominal forms and heaviness of postverbal NP s. The cognitive structure of FVI with lack of S-V concord is argued to be: (1) ground-setter, (2) verb heralding presence/appearance of a generic third-person figure in the ground, (3) nominal identifying the generic figure. Chen’s Invertability Hypothesis is shown to generate false predictions with fronted adjectives and adverbials, and the claim that the preverbal element is in focus is shown to be problematic in the light of its usual status as given information. FVI is argued to be a construction in Goldberg’s (2006) sense of the term, although it does not constitute a meaning-form pairing which is completely independent of the lexical items that instantiate it.

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 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)0.001
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 score0.449
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.288
Teacher spread0.273 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2018
Admission routes1
Has abstractyes

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