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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".