Human Research Ethics Committees in Technical Universities
Bibliographic record
Abstract
Human research ethics has developed in both theory and practice mostly from experiences in medical research. Human participants, however, are used in a much broader range of research than ethics committees oversee, including both basic and applied research at technical universities. Although mandated in the United States, the United Kingdom, Canada, and Australia, non-medical research involving humans need not receive ethics review in much of Europe, Asia, Latin America, and Africa. Our survey of the top 50 technical universities in the world shows that, where not specifically mandated by law, most technical universities do not employ ethics committees to review human studies. As the domains of basic and applied sciences expand, ethics committees are increasingly needed to guide and oversee all such research regardless of legal requirements. We offer as examples, from our experience as an ethics committee in a major European technical university, ways in which such a committee provides needed services and can help ensure more ethical studies involving humans outside the standard medical context. We provide some arguments for creating such committees, and in our supplemental article, we provide specific examples of cases and concerns that may confront technical, engineering, and design research, as well as outline the general framework we have used in creating our committee.
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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: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
| gpt | no category Domain: not available · Genre: Other 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 | 0.352 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.040 | 0.039 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".