Mentoring the Mentors: The Yoda Factor in Promoting Scientific Integrity
Bibliographic record
Abstract
�� The vision to transform scientiac research into a profession that promotes ethical research practices must begin with an educational strategy aimed at strengthening the very life force of the academic research enterprise, namely, the research mentor and mentee relationship. The foremost reason that mentor leadership training should precede teaching research ethics is that ethical value transformation can only be sustained if it occurs at the sociocultural level of the medical research community. Because research mentor relationships make up the infrastructure of the medical research enterprise, they are the key players in the realization of a comprehensive organizational strategy for scientiac integrity in academic research. More than any other factor in the learning process, the partnership of the teacher and the student determines the value of an education. In the nurture of that partnership lies the mentor’s art (Daloz 1999). Implementing a mentor-centered curriculum for teaching research ethics requires genuine institutional commitment to accountability for research integrity at the highest level of academia. The principal way that university commitment is manifested is through the establishment of an Oface for Research Integrity that sponsors innovative educational initiatives and that encompass the substantive issues and the implicit ethical concerns intrin
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".