L’élaboration de savoirs actionnables en PME légitimés dans une conception des sciences de gestion comme des sciences de l’artificiel
Bibliographic record
Abstract
Deux conceptions de la recherche en PME, également légitimes, coexistent actuellement. Selon la première, la recherche vise à élaborer des savoirs scientifiques sur la gestion des PME prises comme objet d’étude. Selon la seconde, elle vise à élaborer des savoirs pour la gestion des PME, c’est-à-dire des savoirs que des dirigeants de PME pourraient exploiter dans leurs décisions d’action. Ces deux finalités, qui sont souvent opposées, ne peuvent-elles vraiment pas être conciliées ? À quelles conditions des savoirs élaborés pour être exploitables par des dirigeants de PME peuvent-ils être considérés comme légitimés scientifiquement ? Certaines méthodes de recherche sont-elles mieux adaptées que d’autres pour élaborer de tels savoirs ?
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".