Bibliographic record
Abstract
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.
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How this classification was reachedexpand
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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".