Responding to the Tragedies of Our Time - The Human Right to Health and the Virtue of Creative Resolve
Bibliographic record
Abstract
We live in tragic times. Millions are sheltering in place to avoid exacerbating the Coronavirus (COVID-19) pandemic. How should we respond to such tragedies? This paper argues that the human right to health can help us do so because it inspires human rights advocates, claimants, and those with responsibility for fulfilling the right to try hard to satisfy its claims. That is, the right should, and often does, give rise to what I call the virtue of creative resolve. This resolve embodies a fundamental commitment to finding creative solutions to what appear to be tragic dilemmas. Contra critics, we should not reject the right even if it cannot tell us how to ration scarce health resources. Rather, the right gives us a response to apparent tragedy in motivating us to search for ways of fulfilling everyone’s basic health needs.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.129 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".