A Harm Reduction Approach to the Ethical Management of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The post-confinement phase of the COVID-19 pandemic will require that governments navigate more complex ethical questions than had occurred in the initial, ‘curve-flattening’ phase, and that will occur when the pandemic is in the past. By looking at the unavoidable harms involved in the confinement and quarantine methods employed during the initial phase of the pandemic, we can develop a harm reduction approach to managing the phase during which society will be gradually reopened in a context of managed risk. The principles that are at the heart of such an approach include a reckoning with all of the harms involved in policy choice, including harms that might be given rise to by policy implementation itself; a focus on the harms to which already vulnerable populations are susceptible; and a strong preference for policies that economize on the use of prohibitions and of coercive state enforcement, and that instead emphasize the agency of citizens in realizing health-promoting behavior change. This framework is applied to a policy proposal that has been discussed in policy circles in a number of countries, that of immunity ‘passports’, and to policies that emphasize the creative use of space and time to achieve physical distancing goals.
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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 | 0.058 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.018 | 0.090 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".