Consensus standards for introductory e-learning courses in human participants research ethics
Bibliographic record
Abstract
This paper reports the results of a workshop held in January 2013 to begin the process of establishing standards for e-learning programmes in the ethics of research involving human participants that could serve as the basis of their evaluation by individuals and groups who want to use, recommend or accredit such programmes. The standards that were drafted at the workshop cover the following topics: designer/provider qualifications, learning goals, learning objectives, content, methods, assessment of participants and assessment of the course. The authors invite comments on the draft standards and eventual endorsement of a final version by all stakeholders.
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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 | 0.541 | 0.602 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".