The Impact of Mentorship on the Career Development of Women in STEM Fields
Bibliographic record
Abstract
This chapter explores the influence of different mentorship models on the career development of women in STEM fields, examining their respective benefits and addressing the challenges and barriers faced by women in accessing and benefiting from these models. It highlights the positive impact of various mentorship approaches, including traditional one-on-one mentorship, peer mentorship, group mentorship, reverse mentorship, virtual mentorship, and formal mentorship programs. These models have been found to enhance women's self-confidence, facilitate career advancement, foster skill development, provide psychosocial support, contribute to long-term career success, and promote leadership development. Challenges identified such as underrepresentation and implicit biases in mentor selection hindered the effectiveness of these mentorship models. The findings underscore the importance of organizations promoting diversity and inclusion, addressing bias, establishing inclusive decentralized platforms, and developing tailored mentorship programs to empower women in STEM fields.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".