Les déterminants de la croissance des essaimages académiques1
Bibliographic record
Abstract
Les essaimages académiques se sont fortement développés en France depuis le début des années 2000. Cependant, malgré ce développement, la compréhension de leur processus de croissance et la durabilité de cette croissance restent peu explorées. À partir d’un échantillon de 118 essaimages académiques français de la même génération, cet article analyse les facteurs qui déterminent leur croissance au-delà de leur 5e année d’existence. Ces facteurs sont dérivés de l’approche par les ressources et des capacités dynamiques. Les résultats de notre étude quantitative montrent que 5 facteurs expliquent la croissance des essaimages académiques étudiés : l’orientation entrepreneuriale (et plus largement les ressources cognitives), l’acquisition de compétences tout au long du processus entrepreneurial, le montant des fonds levés (publics et privés), la capacité technologique et l’accompagnement.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".