Bibliographic record
Abstract
Applications of behavioural economics to public policy are immediate and enlightening. In health policy, where we are exposed to a new fad every other month, it is not indifferent that we deal with a research program that is solidly grounded in decades of scholarship and that is supported by economic theory. Moreover, the experimental and realist bias of behavioural economics is attuned to our need for tested solutions and pragmatic improvements. Adam Oliver's paper is centred on methods that could incite people to make better, healthier lifestyle choices. But his approach can also help us formulate better regulations and smarter legislation. It can help us review the design of health organizations and care pathways. It encourages our efforts to properly use evidence and information. And finally, it forces us to examine the system of incentives and may even give someone the idea of looking at the underlying structure of power.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.035 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".