MétaCan
Menu
Back to cohort
Record W2745049053 · doi:10.4000/pistes.5155

Dérives de la recherche et détresse psychologique chez les universitaires

2017· article· fr· W2745049053 on OpenAlexvenueno aff
Chantal Leclerc, Bruno Bourassa, Christian Macé

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2017
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Les professeures et professeurs des universités québécoises se montrent généralement engagés dans leur travail. Pourtant, de 20 à 25 % d’entre eux révèlent avoir vécu un problème d’ordre psychologique lié à ses conditions d’exercice. Comment reconnaître ce qui peut favoriser ou compromettre la santé psychologique et l’engagement dans une carrière professorale ? Comment prévenir les malaises observés et y remédier ? Pour répondre à ces questions et avoir accès aux expériences se profilant derrière les statistiques disponibles, des entretiens ont été réalisés auprès de 18 groupes de membres du corps professoral ayant accepté de lever le voile sur leurs réalités. L’article présente une partie des données issues de cette démarche en portant une attention particulière à la recherche et à ses dérives. Fatigue, désillusion et détresse sont ressenties lorsque les chercheures et chercheurs doivent se conformer à un modèle unique de performance et aux règles insensées de productivité qu’on leur impose.

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)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.590

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0050.003
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.135
GPT teacher head0.512
Teacher spread0.377 · 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 designObservational
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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePerspectives interdisciplinaires sur le travail et la santéSame topicHealthcare professionals’ stress and burnoutFrench-language works237,207