Missing the Forest: Further Thoughts on the Ethics of Bystander Risk in Medical Research
Bibliographic record
Abstract
Developments in the last several years have sparked renewed interest in the ethics of research involving humans. Issues relating to the global extent of research and its guiding principles are of particular importance to researchers, health officials, and individual ethics committees who want a deeper and more encompassing inquire regarding the foundation and evolution of human research. This department of CQ launches a long overdue effort to explore these wider issues. Readers are invited to submit papers to Charles MacKay, 5011 Worthington Drive, Bethesda, MD 20816, USA. E-mail: mackaycharles@yahoo.com.Without intending to suggest their endorsement of my arguments, I wish to thank my colleagues in the CTRG group and two anonymous referees. This work was funded by the Canadian Institute of Health Research.
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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 | 0.089 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.095 |
| Scholarly communication | 0.017 | 0.038 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.025 | 0.025 |
| 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".