Apprendre malgré l’échec : le cas d’une inéligibilité à la labellisation « pôle de compétitivité »
Bibliographic record
Abstract
Cet article étudie, dans le contexte français de la politique des pôles de compétitivité (logique de cluster), la tentative avortée de création d’un pôle sur le vin à Bordeaux. L’échec est ici analysé comme un inhibiteur versus un déclencheur de l’apprentissage. Peu de travaux abordent ce point dans le champ des clusters industriels, où l’accent est davantage mis sur les succès, tant en théorie qu’en pratique. En dépassant la vision binaire de l’échec, nous proposons une troisième voie, celle d’une dynamique qui se crée malgré l’échec, sans remise en cause des contours du projet. Ceci nous permet de mettre en évidence les conditions d’occurrence d’un tel apprentissage.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".