Écosystèmes entrepreneuriaux : acteurs et interactions des tensions en contradiction
Bibliographic record
Abstract
Écosystèmes entrepreneuriaux : acteurs et interactions des tensions en contradictionLe présent dossier découle du colloque de 2021 de l'Acfas organisé par l'Institut de recherche sur les PME (INRPME) et s'inscrit dans notre volonté de prolonger la discussion et la réflexion sur le thème des écosystèmes entrepreneuriaux (EE), sous l'angle des acteurs et de leurs interactions.Trois journées ont réuni plus de 30 conférenciers/conférencière et près de 20 présentations.Au total, huit propositions d'articles nous ont été envoyées et, après révision, quatre articles composent ce dossier.Les articles retenus accordent une attention particulière au rôle des institutions et des acteurs dont l'importance a été démontrée par les participants et participantes au colloque.
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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".