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Record W4401881220 · doi:10.7202/1112127ar

La recherche en français au coeur des dynamiques de concurrence internationale : la perspective des chercheurs québécois1

2024· article· fr· W4401881220 on OpenAlexaffvenueabout
Olivier Bégin‐Caouette, Cathia Papı, Eya Benhassine

Bibliographic record

VenueEnjeux et société Approches transdisciplinaires · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité TÉLUQUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

L’objectif de cet article est d’examiner l’influence perçue des facteurs qui favorisent ou contraignent la production et la diffusion de recherches en français dans les collèges et les universités du Québec. Les résultats tirés d’un questionnaire (n = 819) et d’entretiens (n = 8) suggèrent que les facteurs qui encouragent l’utilisation du français sont la maîtrise de la langue par les chercheurs et leurs collaborateurs, alors que les facteurs qui limitent cette utilisation sont les collaborations nationales et internationales, de même que le désir d’être lu, cité, reconnu, financé et de faire progresser sa carrière. Les entrevues ont notamment permis de distinguer l’utilisation du français comme langue de travail et comme langue de diffusion. Notre interprétation met en évidence que la langue est un objet social dont l’utilisation dépend de sa maîtrise par les personnes impliquées et des impératifs disciplinaires plutôt que des directives institutionnelles ou gouvernementales.

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)0.017
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0130.011
Scholarly communication0.0110.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.480
GPT teacher head0.553
Teacher spread0.073 · 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.

Study designQualitative
DomainIncentives
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 routes3
Has abstractyes

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