Professional know-how and MSD prevention: conceptual and methodological reflection leading to their identification and the start of their construction
Bibliographic record
Abstract
Repetitive work is often seen as work that can be learned by simple observation of a colleague. This study aimed to describe the know-how and especially the know-how that can be used for one’s protection, developed by six expert workers in a meat-cutting department. Individual and group interviews, on-site and video observations, and individual auto-confrontation (confronting participants with their own activity) were conducted. A typology of the know-how’s knowledge base was developed and the existence of a hierarchy in the development of expertise was brought to light, including know-how called « efficient know-how ». This hierarchy highlights all the complexity of the work that should be considered in organizing training and presenting content. The ergonomic approach developed in this study and a theoretical framework resulting from practice have helped to uncover the wealth of knowledge that is mobilized in manual labor.
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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 | 0.084 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".