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Record W4399411143 · doi:10.25145/j.cedille.2024.25.08

Les termes prévenu et accusé en droit pénal français, canadien et suisse et leurs équivalents roumains

2024· article· en· W4399411143 on OpenAlexaboutno aff

Bibliographic record

VenueÇédille · 2024
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)SuspectHumanitiesCriminal codeRomanianCode (set theory)SociologyLawPolitical scienceLinguisticsPhilosophyCriminal lawSet (abstract data type)Computer science

Abstract

fetched live from OpenAlex

In French criminal law, the various procedural statuses of (alledged) offenders are highly nuanced: suspect, témoin, témoin assisté, mis en examen, prévenu, accusé , etc. The concepts operationalised in this respect in the codes (Code pénal and Code de procédure pénale in partic ular) are not systematically defined, but the oppositions that structure this terminological field can easily be approached with the help of contexts and cotexts (the study of collocations proves very promising in this respect). The same is true of the opposition that is the focus of our research (prévenu vs. accusé). e two notions are culturally marked: our study will explore the differences in use (and therefore in conceptualisation / designation) of the two terms in French, Swiss and Canadian criminal law, while evoking the intercultural Romanian equivalents of the terminological system from which they derive.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0060.016
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.109
GPT teacher head0.318
Teacher spread0.209 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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