Prioritizing Mentorship as Scientific Leaders
Bibliographic record
Abstract
Scientific careers are rarely straight paths. This article emphasizes the crucial role of mentorship in navigating scientific careers and sustaining innovation in STEMM fields. Effective mentorship can have a positive impact on graduate students' research productivity, research self-efficacy, degree completion, and program satisfaction. Despite its importance, mentorship is often an overlooked and underappreciated component of scientific training. As members of the 2022 CAS Future Leaders class, representing ten countries and various chemistry subdisciplines, we share our mentorship experiences to suggest actions to promote healthy and inclusive mentor-mentee relationships in chemistry. The article explores the importance of mentorship, outlines impactful strategies, and offers insights into how to create a scientific community that values and prioritizes effective mentorship.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".