Hopes for Helsinki: reconsidering “vulnerability”
Bibliographic record
Abstract
The Declaration of Helsinki is recognised worldwide as a cornerstone of research ethics. Working in the wake of the Nazi doctors’ trials at Nuremberg, drafters of the Declaration set out to codify the obligations of physician-researchers to research participants. Its significance cannot be overstated. Indeed, it is cited in most major guidelines on research involving humans and in the regulations of over a dozen countries. Although it has undergone five revisions,1 and most recently incorporated (albeit controversial) language aimed at addressing concerns over research carried out in resource-poor countries,2–5 the Declaration could go much farther in addressing the profoundly altered landscape of research with humans. Research involving humans is now a global enterprise and often involves participants from resource-poor countries. Rather than being carried out at single institutions by veteran researchers, many studies are now conducted at many locations—including sites that are not academic medical centres—by new and relatively inexperienced investigators. A growing number of projects involve novel agents, based on innovative work in genomics and proteomics. Increasingly, research is sponsored by the for-profit sector. National governments and professional organisations around the globe provide laws, regulations and standards for the conduct of research involving humans. Considerable scholarship also critiques and guides this endeavour. In light of the current effort of the World Medical Association (WMA) to revise the Declaration, we offer ideas on how to re-conceive the concept of “vulnerability” and its links with the principle of justice and, in turn, redirect the attention of researchers towards those who might be so designated. In the research context, “vulnerability” is associated with an inability partly or totally to protect one’s own interests. Typically, conceptions of vulnerability centre upon characteristics associated with particular groups (such as children, prisoners, indigenous people, those who are ill and the poor) that …
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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 | 0.350 | 0.451 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.129 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.029 | 0.073 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".