Mentoring as professional development for novice entrepreneurs: maximizing the learning<sup>1</sup>
Bibliographic record
Abstract
Mentoring can be seen as relevant if not essential in the continuing professional development of entrepreneurs. In the present study, we seek to understand how to maximize the learning that occurs through the mentoring process. To achieve this, we consider various elements that the literature suggested are associated with successful mentoring and test a comprehensive model with the main parts of the mentoring process. Using a structural equation model on a sample of 360 Canadian‐mentored entrepreneurs, the study demonstrates that mentor's career‐related functions are the most effective factor in the development of learning, followed by psychological functions and the role model function. In order to foster these functions, trust and perceived similarity are needed to build a strong and high‐quality relationship as is mentee self‐disclosure. These results are of interest because they highlight the different elements that influence learning through mentoring and show the mediating role of trust, perceived similarity and mentor's functions between the mentee's self‐disclosure and learning.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".