Where are the Athenas? Mentors' Gender Talk and Prospects for Equality
Bibliographic record
Abstract
Research has suggested various explanations for an ongoing asymmetry in the benefits of corporate mentoring for men over women. Drawing on interviews with senior executive mentors, both women and men, in three large firms that differ in terms of industrial and occupational distribution along gender lines, we offer fresh insight into the puzzle of why this imbalance persists. First, asking how mentors engage gender in their practice, we illuminate what we term ‘gender talk,’ and discover varying modes of denial about gender difference that enable inequities between men and women relating to both public (‘work’) and private (‘life/family’) spheres, to remain largely unopposed within this prominent practice. Second, mentoring scholars have called for theory in this domain that attends to social contexts and their variability. Drawing on the literature in gender and organizations and our comparative research design, we offer one such theory, that of mentoring as a
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".