A Relational Account of Public Health Ethics
Bibliographic record
Abstract
Recently, there has been a growing interest in public health and public health ethics. Much of this interest has been tied to efforts to draw up national and international plans to deal with a global pandemic. It is common for these plans to state the importance of drawing upon a well-developed ethics framework and we argue that this framework should reflect the values and insights of feminist relational theory. More specifically, we argue that pandemic planning must be squarely situated in the larger realm of public health and that an ethics framework for public health will be one that recognizes the need to pay particular attention to the vulnerability of subpopulations lacking in social and economic power. We propose an ethics framework for public health that builds on the notions of relational personhood (including relational autonomy and social justice) and relational solidarity. In this way, we aim for a public health ethics that, as appropriate, promotes the public interest and the common good.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".