Bibliographic record
Abstract
Devant les difficultés, les entrepreneurs se doivent de toujours persévérer, mais en pratique, la plupart d’entre eux passent par des périodes de doute au cours desquelles leur engagement se fait plus incertain. Paradoxalement, ces remises en question peuvent contribuer à la perpétuation de l’activité entrepreneuriale : s’arrêter et prendre le temps de douter peut permettre aux entrepreneurs de faire le point sur leurs attentes et de redécouvrir l’entreprise sous un nouveau jour. C’est très souvent à cette condition que les entrepreneurs reconstruisent l’engagement nécessaire pour poursuivre l’entreprise : un engagement souvent très différent de celui qui avait motivé sa création. À partir de 50 entretiens, cet article propose un modèle en quatre étapes : engagement anticipé, engagement dans l’action, engagement confus et engagement du second souffle. Partant de là, nous invitons les accompagnateurs et les partenaires du projet à soutenir les entrepreneurs dans ces moments difficiles afin de leur laisser le temps de retrouver la force de continuer.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".