Building and sustaining mentor interactions as a mentee
Bibliographic record
Abstract
Mentorship is experience and/or knowledge‐based guidance. Mentors support, sponsor and advocate for mentees. Having one or more mentors when you seek advice can significantly influence and improve your research endeavours, well‐being and career development. Positive mentee–mentor relationships are vital for maintaining work–life balance and success in careers. Early‐career researchers (ECRs), in particular, can benefit from mentorship to navigate challenges in academic and nonacademic life and careers. Yet, strategies for selecting mentors and maintaining interactions with them are often underdiscussed within research environments. In this Words of Advice, we provide recommendations for ECRs to seek and manage mentorship interactions. Our article draws from our experiences as ECRs and published work, to provide suggestions for mentees to proactively promote beneficial mentorship interactions. The recommended practices highlight the importance of identifying mentorship needs, planning and selecting multiple and diverse mentors, setting goals, and maintaining constructive, and mutually beneficial working relationships with mentors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".