Bibliographic record
Abstract
Paying people to engage in healthy behaviours, such as adhering to medications, quitting smoking and losing weight, has been linked to the nudge agenda. However, "user financial incentives" (UFI) can only be classified as nudges if they meet a strict set of requirements. Perhaps more importantly, UFI have thus far showed some promise only for "single shot" behaviour change, such as that associated with many acts of medical adherence, and have been generally unfruitful in effecting the sustained behaviour change that is necessary to influence broader lifestyle decisions, such as those associated with smoking and weight. Possibly more importantly still, the legitimacy of government-sponsored interventions intended to influence directly broad lifestyle behaviours, providing that those behaviours are not unduly harming others, ought to be scrutinized
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 | 0.046 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.033 | 0.033 |
| Insufficient payload (model declined to judge) | 0.011 | 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".