Comment améliorer la pertinence de la recherche en gestion?
Bibliographic record
Abstract
L’écart entre la rigueur et la pertinence des recherches en gestion fait l’objet de nombreux débats. Initialement lancés en Amérique du Nord dans les années 80, ces débats apparaissent en France depuis une décennie. Afin de mieux relever les défis du transfert des connaissances, nous proposons d’abord aux chercheurs des moyens de favoriser davantage la pertinence à toutes les étapes de leurs recherches. Ensuite, nous traitons des changements contextuels nécessaires pour concilier davantage les critères de pertinence et de rigueur ainsi que le transfert des connaissances au sein des organisations et de la société. Enfin, nous montrons comment les nombreux changements économiques, concurrentiels, sociologiques et technologiques favorisent la recherche de pertinence et la synergie « recherche/enseignement/transfert ».
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".