Bibliographic record
Abstract
Abstract Entrepreneurial mentoring is a source of learning for novice entrepreneurs. This chapter investigates the specific role of mentor functions, namely psychosocial, career-related, and role-model functions, to support entrepreneurial learning. This research also looks at the impact of similarity in dyads, perceived and real (gender, career, industry), to foster learning. The authors recruited 412 mentees through Réseau Mentorat, the largest mentoring network in Québec (Canada). They found that psychosocial functions are the most important aspect to foster any kind of learning in entrepreneurial mentoring relationships. Career-related functions are also very important for two kinds of learning, namely learning entrepreneurial tasks and learning to manage SME development. The role-model function is only relevant to support the learning to manage SME development. The results highlight potential gender stereotypes that could be at play, as male mentees show less learning for entrepreneurial tasks when paired with a female mentor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".