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Record W4414516464 · doi:10.1080/08989621.2025.2560886

A classroom exercise for improving mentor/mentee relationships

2025· article· en· W4414516464 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAccountability in Research · 2025
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsColumbia College
Fundersnot available
KeywordsPhysical activityComponent (thermodynamics)Research ethics

Abstract

fetched live from OpenAlex

BACKGROUND: Responsible Conduct of Research (RCR) courses seek to heighten awareness of the importance of mentor/mentee interactions and other topics, but questions remain - e.g., how best to train mentors/mentees to establish such relationships. DESCRIPTION OF EXERCISE: This paper proposes an approach as a model to strengthen RCR education by more fully, and actively, rather than passively, engaging trainees. A classroom activity was developed that can enhance instructors' abilities to improve mentor/mentee interactions. The instructor divided classes into groups of roughly four trainees, and had them think of a good mentor they have observed, and to list traits/behaviors they liked. Groups then summarized discussions for the class. The instructors recorded and integrated responses. Each group then considered bad mentors, answering the same questions, and repeating the process regarding bad mentees and good mentees. The class then compared the four discussions. Trainees have commonly had both formal and informal mentors, seen both good and bad mentors and mentees, and often themselves served as mentors. Mentees thus connect abstract principles concerning mentorship to personal experiences; and reflect on their own interactions/roles, preferences, and rights/responsibilities. CONCLUSION: This exercise suggests some benefits of recognizing personal/emotional, not just intellectual components in RCR, and has important implications for education, practice, and research.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Incentives · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.242
GPT teacher head0.497
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it