Bibliographic record
Abstract
This paper examines the nature of the harm-benefit tradeoff in early clinical research for interventions that involve remote possibility of direct benefit and likelihood of direct harms to research participants with fatal prognoses, by drawing on the example of gene transfer trials for glioblastoma multiforme. We argue that the appeal made by the component approach to clinical equipoise fails to account fully for the nature of the harm-benefit tradeoff-individual harm for social benefit-that would be required to justify such research. An analysis of what we label "collateral affective benefits," such as the experience of hope or exercise of altruism, shows that the existence of these motivations reinforces rather than mitigates the necessity of justification by reference to social benefit. Evaluations of social benefit must be taken seriously in the research ethics review process to avoid the exploitation of research participants' motivations of hope or altruism and to avoid the possibility of inadvertent exploitation of high-risk research participants and the harms that would associate with such exploitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.456 | 0.597 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.016 | 0.012 |
| 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; the direct Gemma label and the distilled Codex classifier 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".