Is More Truly Merrier?: Mentoring and the Practice of Law
Bibliographic record
Abstract
Cet article examine deux questions sur le mentorat. D'abord, qui est le plus susceptible de bénéficier de services de mentorat au cours de sa carrière? Ensuite, comment le mentorat influence‐t‐il les carrières des professionnels? En utilisant une enquête longitudinale sur des avocats, les auteures évaluent l'incidence des postes et des aspirations en début de carrière sur les chances de bénéficier de services de mentorat. Elles mesurent les bénéfices du mentorat au moyen des récompenses de carrière intrinsèques et extrinsèques, pour découvrir que le contexte organisationnel et les attributs individuels constituent d'importants prédicteurs de qui bénéficiera de mentorat. Les professionnels ayant de multiples mentors se sont avérés les grands gagnants, en ce qu'ils obtiennent des récompenses de carrière plus importantes et plus diversifiées que ceux n'ayant pas de mentor. This paper addresses two questions regarding mentoring: First, who is most likely to receive mentorship during their career? And second, how does mentorship shape the careers of professionals? Using a longitudinal survey of lawyers, we evaluate the impact of early career positions and aspirations on the chances for mentorship. We assess the benefits of mentorship across extrinsic and intrinsic career rewards. We find organizational context and individual attributes are important predictors of who receives mentorship. Professionals with multiple mentors were the big winners in that they obtain greater and more diverse career rewards over those with one or no mentors.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".