Bibliographic record
Abstract
Let us start with an example of health policy analysis in action. Within that category of countries loosely known as ‘the West’, quite basic differences exist in attitudes to health policy and also actual health policy. Comparing the US with mainland Europe and indeed Canada, for example, one perceives a difference in attitude on the part of the majority towards collectivism and individualism in access to, provision of and financing of healthcare. The explanation for policy and system differences—for example, between the US healthcare system(s) and the various NHSs of the UK countries (England, Scotland, Wales and Northern Ireland)—is commonly framed in terms of ‘ideology’ but there are also ‘institutional’ explanations (1). Additionally, however, popular attitudes or ‘values’ may be taken as autonomous ‘inputs’ into the explanation (e.g. ‘American values prevent the enactment of an NHS’) or, at least in part, derived from or influenced by institutional reality. If, for example, there is no chance of a bill to establish an NHS or a comprehensive system of public health insurance passing in Washington, then reformers over time trim not only their legislative ambitions, but also their very way of thinking about the issue.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".