Is the Cure Worse than the Disease? The Ethics of Imposing Risk in Public Health
Bibliographic record
Abstract
Efforts to improve public health, both in the context of infectious diseases and non-communicable diseases, will often consist of measures that confer risk on some persons to bring about benefits to those same people or others. Still, it is unclear what exactly justifies implementing such measures that impose risk on some people and not others in the context of public health. Herein, we build on existing autonomy-based accounts of ethical risk imposition by arguing that considerations of imposing risk in public health should be centered on a relational autonomy and relational justice approach. Doing so better captures what makes some risk permissible and others not by exploring the importance of power and context in such deliberations. We conclude the paper by applying a relational account of risk imposition in the cases of (a) COVID-19 measures and (b) the regulation of sugar-sweetened beverages to illustrate its explanatory power.
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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 | 0.147 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.047 |
| 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".