Pertinence du tutorat comme dispositif d’accompagnement du repreneur individuel après la reprise. Une étude empirique à l’échelle européenne
Bibliographic record
Abstract
Si l’accompagnement entrepreneurial est un champ de recherche en croissance, il existe encore peu de travaux sur l’accompagnement « repreneurial ». Cet article s’intéresse donc à la spécificité des besoins des repreneurs en matière d’accompagnement post-reprise et aux formes que cet accompagnement peut prendre. S’appuyant sur un projet test réalisé à l’échelle européenne sur un échantillon de 889 reprises, les auteurs confirment empiriquement la pertinence du tutorat comme forme particulière d’accompagnement dans cette phase délicate. Ils montrent ensuite dans quels types de situation et pour quels types de repreneur un tutorat s’avère le plus bénéfique.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".