Marrying Human Rights and Health Care Systems: Context for a Power to Improve Access and Equality
Bibliographic record
Abstract
This is an introduction to a volume, in which we explore the power of health care rights in diverse health care systems. Does a right to health care serve to advance greater equity or does it in fact advance the opposite result? Does the recognition of a right to health care help sustain public values (like equality) in systems that are undergoing privatization? Or, to the contrary, does a focus on right-based norms foster individualism and excerbate inequalities brought about by privatization? Does the legal means by which health care rights are established make a difference (whether in a constitutional document, in a statue, etc.)? How do courts balance the rights of an individual against collective of health rights protections? To what extant are broader legal, economic, and political considerations taken into account in the courts' reasoning about health rights? Does the interpretation of the right to health vary depending on the model of health system involved (e.g., private insurance, social insurance, single payer [public/tax-financed])?
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".