L’évaluation, un outil de l’ergonome pour transformer le contexte d’intervention
Bibliographic record
Abstract
Cet article expose la mise en œuvre d’une recherche-action visant à explorer si une démarche évaluative de type recherche-évaluative permet d’identifier les facteurs favorables à une intervention et d’instruire les hypothèses des intervenants sur les liens entre processus d’intervention et résultats obtenus. Les cas utilisés illustrent comment la recherche évaluative peut permettre à l’ergonome d’améliorer les conditions dans lesquelles il intervient en partageant des indicateurs d’évaluation sur le déroulement du projet. En communiquant avec les autres acteurs du projet sur les indicateurs de pilotage d’intervention, l’ergonome modifie le contexte dans lequel il intervient au cours du déroulement de l’action. En utilisant également des indicateurs de résultats, l’ergonome peut montrer les apports de l’action et chercher à mieux adapter ses méthodes compte tenu des leviers ou obstacles présents dans le contexte d’action.
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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 | 0.143 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".