“Lives versus livelihoods”: Conflict and coherence between policy objectives in the COVID-19 pandemic
Bibliographic record
Abstract
Many policies were put in place during the COVID-19 pandemic in the United States to manage the negative impact of the coronavirus. Limiting severe illness and death was one important objective of these policies, but it is widely acknowledged by public health ethicists that pandemic policies needed to consider other factors. Drawing on semi-structured interviews with 38 people across 17 states who participated in the state-level COVID-19 pandemic policy process, we examine how those actors recounted their engagement with four different objectives over the course of the pandemic: protecting public health with respect to COVID-19 (which we refer to as pathogen-focused disease prevention), protecting the economy, promoting the public's broader health and wellbeing, and preserving and restoring individual freedoms. We describe the different ways that pathogen-focused disease prevention was thought to have conflicted with, or to have been coherent with, the other three policy objectives over the course of the pandemic. In tracing the shifting relationships between objectives, we highlight four reasons put forward by the participants for why policy changes occurred throughout the pandemic: a change on the part of decisionmaker(s) regarding the perceived acceptability of the negative effects of a policy on one or more policy objectives; a change in the epistemic context; a change in the 'tools in the toolbox'; and a change in the public's attitudes that affected the feasibility of a policy. We conclude by considering the ethical implications of the shifting relationships that were described between objectives over the course of the pandemic.
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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 | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.082 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".