Mentoring Women in STEM Careers: The Reciprocal and Agentic Role of Context
Bibliographic record
Abstract
Mentoring discourse has a long history. There are many different approaches to mentoring but most fail to account for the impact that contexts can have on how mentoring is enacted. This is problematic because, without this, it is difficult to properly evaluate the impact of the mentoring itself. We use a case study research design to critically evaluate a recent mentoring intervention, carried out in Arequipa, Peru. Using theories of institutional theory (Paik et al., 2011), power (Clegg, 1989), reciprocal mentoring (Haddock-Millar et al., 2024) and agency (Bencherki et al., 2024), we explore the contextual issues at play and develop a framework which explains why there are significant differences in emphasis and focus between mentoring definitions, programs and initiatives.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".