Enseigner les outils de gestion : proposition d’une méthode pédagogique de dissection
Bibliographic record
Abstract
Les outils de gestion, enseignés comme des boîtes à outils apportant des solutions à des problèmes clairs, peuvent participer d’un "mythe représentationniste". Or, dans la réalité, ils sont, en tant que représentations nécessairement simplificatrices de l’activité, en tension sémiotique avec les situations de gestion. Comment imaginer un enseignement des outils de gestion qui ne masque pas, mais, au contraire met à jour cette tension représentationnelle et sémiotique qui leur est inhérente ? Cet article propose une méthode de dissection, facile à mettre en oeuvre dans la salle de classe, en cinq étapes : sélection, inscription, (in)invisibilisation, performativité, et re-conception. Un cas pratique est présenté en troisième partie, qui montre que cette méthode, en mettant à jour la tension sémiotique autour des outils de gestion, permet de faire le lien avec les enjeux sociétaux et organisationnels.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".