Bibliographic record
Abstract
COVID-19-related controversies concerning the allocation of scarce resources, travel restrictions, and physical distancing norms each raise a foundational question: How should authority, and thus responsibility, over healthcare and public health law and policy be allocated? Each controversy raises principles that support claims by traditional wielders of authority in "federal" countries, like federal and state governments, and less traditional entities, like cities and sub-state nations. No existing principle divides "healthcare and public law and policy" into units that can be allocated in intuitively compelling ways. This leads to puzzles concerning (a) the principles for justifiably allocating "powers" in these domains and (b) whether and how they change during "emergencies." This work motivates the puzzles, explains why resolving them should be part of long-term responses to COVID-19, and outlines some initial COVID-19-related findings that shed light on justifiable authority allocation, emergencies, emergency powers, and the relationships between them.
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 | 0.032 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".