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Record W1531227189 · doi:10.4000/pistes.1852

Suivre les évolutions du travail et de la santé : EVREST, un dispositif commun pour des usages diversifiés

2011· article· fr· W1531227189 on OpenAlexvenueno aff
Anne-Françoise Molinié, Ariane Leroyer

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2011
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Le dispositif Evrest (Évolutions et RElations en Santé au Travail) est un observatoire pluriannuel par questionnaire, construit en collaboration par des médecins du travail et des chercheurs, pour pouvoir analyser et suivre différents aspects du travail et de la santé de salariés. Il vise à constituer une base nationale, sur un échantillon de salariés, pour des travaux à caractère scientifique, mais aussi à permettre à chaque médecin (ou à des médecins qui se coordonneraient) d’utiliser Evrest au-delà de l’échantillon national en fonction de ses préoccupations. Sa construction s’inscrit dans un processus visant à rendre compatibles des exigences propres à chacun de ces « mondes » professionnels. Elle passe par la création et le partage d’« objets », partage dans lequel chacun « retrouve son compte », mais aussi par l’élaboration progressive de formes nouvelles de coordination et d’échanges.

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)0.012
metaresearch head score (Gemma)0.022
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 score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0030.005
Scholarly communication0.0080.008
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.063
GPT teacher head0.433
Teacher spread0.370 · 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

Citations17
Published2011
Admission routes1
Has abstractyes

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