Building Capacity for Research Ethics: Policy Insights For Nepal
Bibliographic record
Abstract
Knowledge and skills in research ethics are essential for conducting ethically responsible research. Despite some local policies, strategic guidelines, and manuals on the antithesis of research misconduct, researchers’ adherence to research ethics, especially ethically responsible conduct of research, is still critical in developing countries like Nepal. This study explores the policy provisions to develop researchers’ capacity on research ethics in Nepal. With the aim, we identified ten key documents related to research ethics from the University Grants Commission (UGC), Nepal Health Research Council (NHRC), and Ministry of Education, Science and Technology (MoEST). We analysed the provisions using the adapted version of Cooke’s framework for research capacity development. The result shows that although there are provisions for capacity development for scientific research, none provisioned that the higher education research institutions need to take action for developing early career researchers’ capacity on research ethics. Further, this review depicts several structural, institutional, and procedural limitations that make the condition difficult to adopt and implement those policies, strategic guidelines, and manuals.
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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 | 0.054 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.011 |
| 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".