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Record W2091632827 · doi:10.1075/li.26.1.03lam

Les notions linguistiques de figement et de contrainte

2003· article· en· W2091632827 on OpenAlexaboutno aff
Béatrice Lamiroy

Bibliographic record

VenueLingvisticae Investigationes · 2003
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsExpression (computer science)Variation (astronomy)Computer scienceHistoryPhilosophyPhysics

Abstract

fetched live from OpenAlex

Summary The paper addresses the question of how the notion of fixed expression or idiom has to be defined. Usually idioms are defined as expressions which are characterized by semantic opacity, lack of lexical (paradigmatic) variation and morphosyntactic constraints. However, so-called ‘free’ (i.e. non idiomatic) expressions can be shown to bear similar lexical and morphosyntactic constraints, so that the limit between ‘fixed’ and ‘free’ expressions is much less clear-cut than one would expect. The only real difference which opposes idioms from ‘non-idioms’ is semantic opacity. This theoretical problem is illustrated in the paper by a case study of regional French expressions belonging either to Quebec French, Belgian French, Swiss French or French from France. The latter research is part of a large project (BFQS-project) which aims at the recollection and syntactic description of all French idiomatic expressions used in Europe and/or North-America.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.013
Scholarly communication0.0080.009
Open science0.0010.002
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.076
GPT teacher head0.336
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations24
Published2003
Admission routes1
Has abstractyes

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