Mentoring Relationships: A Mentee’s Journey
Bibliographic record
Abstract
[Extract] Mentoring relationships are ideally driven by mentees and help mentees to tackle professional challenges and/or plan career development. Mentees can choose from several mentoring formats: senior, near peer, within or outside the institution, dyadic or network, in person or virtual. Regardless of the format, mentors guide key stages of mentee professional development, balance challenging and supporting the mentee, and help the mentee to reflect and make informed decisions. The figure below illustrates how a mentee starts the journey by identifying potential mentors, meets the mentor to discuss aspirations and challenges, and formulates next steps guided by the mentor. The mentee is the architect, and the mentor is the facilitator and guide. A short- or long-term mentoring relationship can be forged based on goals and compatibility. As depicted by the ladders in the figure below, mentoring relationships may require backward jumps and restarts to deal with changing circumstances.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".