Bibliographic record
Abstract
Cet article propose d’étudier les « masques » de la pénibilité du travail d’ingénieur. Il s’appuie sur une enquête qualitative menée au sein de trois grandes entreprises des secteurs automobile, aéronautique et de l’énergie. Examinant ces pénibilités, il montre que leur invisibilité tient pour beaucoup à la qualité des conditions d’emploi des salariés étudiés ainsi qu’à leur socialisation scolaire et professionnelle à un univers hautement compétitif. Il tente également de révéler les conditions qui rendent possible la sortie de l’invisibilité des liens entre santé et travail, au niveau individuel, comme au niveau collectif. Enfin, il met en évidence comment une activité, qui mobilise avant tout des capacités cognitives et intellectuelles, engage également les corps des travailleurs : ils cristallisent les marques des pénibilités du travail, que les contraintes vécues soient d’ordre physique ou mental, et obligent les ingénieurs à prendre conscience des raisons de leurs maux lorsque leurs limites sont atteintes.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".