Empirical population and public health ethics: A review and critical analysis to advance robust empirical-normative inquiry
Bibliographic record
Abstract
The field of population and public health ethics (PPHE) has yet to fully embrace the generation of evidence as an important project. This article reviews the philosophical debates related to the 'empirical turn' in clinical bioethics, and critically analyses how PPHE has and can engage with the philosophical implications of generating empirical data within the task of normative inquiry. A set of five conceptual and theoretical issues pertaining to population health that are unresolved and could potentially benefit from empirical PPHE approaches to normative inquiry are discussed. Each issue differs from traditional empirical bioethical approaches, in that they emphasize (1) concerns related to the population, (2) 'upstream' policy-relevant health interventions - within and outside of the health care system and (3) the prevention of illness and disease. Within each theoretical issue, a conceptual example from population and public health approaches to HIV prevention and health promotion is interrogated. Based on the review and critical analysis, this article concludes that empirical-normative approaches to population and public health ethics would be most usefully pursued as an iterative project (rather than as a linear project), in which the normative informs the empirical questions to be asked and new empirical evidence constantly directs conceptualizations of what constitutes morally robust public health practices. Finally, a conceptualization of an empirical population and public health ethics is advanced in order to open up new interdisciplinary 'spaces', in which empirical and normative approaches to ethical inquiry are transparently (and ethically) integrated.
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 | 0.072 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".