Working with multidisciplinary or non-academic collaborators as mentors
Bibliographic record
Abstract
Mentoring relationships in academia are traditionally constructed as hierarchies, where a supervisor mentors a trainee, or an advisory committee guides a trainee. We propose that all collaborations are mutual mentorship opportunities, where all people involved learn from each other while working towards a common goal. Moreover, researchers and trainees can be mentored or learn from non-academic mentors in different disciplines or sectors. Herein we outline a tutorial on how to break down a research project into stages, and the logistics and value of engaging mentors or collaborators from different sectors and disciplines at each stage, and how multidisciplinary or non-academic collaborators can provide mentoring to support trainee learning and academic success.
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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 | 0.043 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".