Research Ethics as Social Policy. Some Lessons from Experiences in Canada and in the United States
Bibliographic record
Abstract
For more than tlrree decades, Canada and the United States have used similar mechanisms for ensuring the protection of human subjects involved in biomedical and behavioural research: written guidelines that specify the substantive and procedural requirements of investigators and institutions; individual informed consent, and prior review and approval by interdisciplinary committees. Given the proximity of the countries to one another and the massive amount of trade and commerce that transpires between them, it is not surprising that these countries share a number of values in research. During the past fifteen years, however, both countries have experienced new challenges to their systems. Sorne of the challenges relate to new trends in research, such as genetics studies and massively increased private sector funding for pharmacological research. Other challenges relate to emerging trends in oversight policies and procedures, such as accreditation of ethics committees. Research reflects a country's particular social policies. The responses to emerging trends illustrate how such policies are evolving in sometimes quite different ways in both countries. This reflects the related but distinct political cultures and structures in the two countries. This paper will explore these trends and emerging responses, drawing lessons from each.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".