Les entrepreneurs ont-ils cru dans leur(s) mythe(s) ? Mettre en dialogue l’entrepreneuriat et l’effondrement
Bibliographic record
Abstract
Le mythe de l’entrepreneur paraît sévir sans pour autant qu’on paraisse le saisir tant il confond héroïsme et figure individuelle. Qui est vraiment dupe, nous suggère Paul Veyne ? Cet essai interroge la tension entre mythification et mystification à l’ère d’un renouvellement des mythes au profit de la startup. Ce renouvellement du mythe contribue-t-il à la reproduction d’un « monde mauvais » ou est-il réparateur face à l’effondrement ? L’essai interroge également la performance ou la réalité du mythe pour les populations vulnérables ou mises aux marges de l’entrepreneuriat mais aussi le piège d’une réflexion centrée sur les identités au mépris des vies. Finalement, l’essai nous interroge sur les possibilités d’un activisme entrepreneurial, porteur d’imaginaires, ou d’un entrepreneuriat politique, créateur de relations avec et dans le vivant, qui porterait les possibilités ou dérives d’une autre mythologie.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".