Are you my mentor? Informal mentoring mutual identification
Bibliographic record
Abstract
Purpose The purpose of this study is to understand the extent to which potential mentors and protégés agree that an informal mentoring relationship exists. Because these relationships are generally tacitly understood, either the mentor or protégé could perceive that there is a mentoring relationship when the other person does not agree. Whether gender affects this is also to be examined. Design/methodology/approach Individuals were asked to identify their mentoring partners. Each report of a partner was then compared to the partner's list to determine whether there was a match (i.e. both reported the relationship as an informal mentoring relationship) or a mismatch (i.e. where one partner reported the relationship as an informal mentoring relationship but the other did not). This pattern of matches and mismatches was then analyzed to determine level of matching and gender differences. Findings There is little agreement between mentoring partners: neither potential protégés nor potential mentors were very accurate at identifying reciprocal informal mentoring partners. However, gender was not found to be related to different levels of matching. Originality/value Previous work has not examined whether potential informal mentoring partners perceive the relationship in the same way. This has implications for employees who are depending upon their mentoring partners for support that may not be forthcoming because the partner does not view the relationship similarly. The findings also have implications for researchers, particularly when studying mentoring relationships from only one perspective and implicitly assuming agreement between partners.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".