Bibliographic record
Abstract
Administrative law comprises the rules, values, and processes by which government and regulatory decision-making is subject to administrative monitoring, review, and accountability. It impacts public health in two ways: through the design, powers, and processes of institutions that enforce administrative law; and through the substantive rules of administrative law. Yet despite its fundamental regulation of the way in which public health decisions are made, insufficient research has been conducted on administrative law as a determinant of public health. Administrative law and public health operate as siloed academic disciplines with very little cross-disciplinary collaboration, engagement, or understanding. This results in major, untapped research opportunities exploring how administrative law could contribute to an optimized model of planetary health in both higher income and lower-middle income countries. Put simply, a holistic, global view of the determinants of public health must take due account of the accountability rules and controls that regulate how public health, and other, decisions are made. This commentary is a call to action to better understand how administrative law mechanisms, such as judicial review, administrative tribunals, ombudsmen, information commissioners, public auditors, and human rights monitors, can be designed or redesigned to better promote sustainable public health outcomes.
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 | 0.025 | 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 | 0.010 | 0.054 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".