Factors Leading to Satisfaction in a Mentoring Scheme for Novice Entrepreneurs
Bibliographic record
Abstract
Mentoring is rapidly gaining in popularity as a customized way to assist and support the novice entrepreneur. However, we still do not know very much about the usefulness of this approach or the benefits perceived by the mentees. The purpose of this study is to share evaluation data associated with a formal mentoring program, with respect to those factors that are likely to influence mentees’ satisfaction with their mentoring experience. Data was collected from 142 entrepreneurs who participated in a formal mentoring program designed for novice entrepreneurs by the Fondation de l'Entrepreneurship in Quebec, Canada. Results show that it is very important for the mentee to feel that his/her mentor truly understands what he/she is going through. Trust is of utmost importance and both the mentor and his/her mentee have to respect the "moral contract" they established at the beginning of the relationship. Finally, the mentee expects the mentoring relationship to produce visible results in his/her firm.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".