PUBLIC HEALTH, PUBLIC HEALTH ETHICS PRINCIPLISM, AND GOOD GOVERNANCE DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract The COVID-19 pandemic brought about at least two normative challenges on unprecedented scale for liberal democracies. One concerned prioritization decisions when health care resources were constrained. The other, which arguably led to lasting damage to social cohesion and citizens’ trust in government and government public health institutions, concerned policies introduced with the aim of reducing the spread of SARS-CoV2, some of which turned out to be mistaken. I discuss in this essay a few examples of misguided, liberty-limiting public health policies and describe how public health and public health ethics principlism provided cover for such policies. Citizens had reasons to be concerned about the duration of such liberty-infringing policies, the absence of predictable government policies, and the absence of transparent justifications for the policies that were implemented.
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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 | 0.057 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.095 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| 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; 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".