Behavioural knowledge for policy design: The connection between time use Behaviours and (or) desires and support for policy alternatives
Bibliographic record
Abstract
Abstract The study explored how understanding people's behaviours and desires can inform policy design and contribute to policy feedback theory. We focused on uses of time that are affected by diverse policies. Given the growing interest in promoting well‐being and the connection between the use of time and well‐being, we examined behaviours and desires regarding uses of time. In this exploratory study, we employed a quantitative research method. We surveyed 671 Israeli adults on their time use, desires for time use, and support for policy alternatives in three policy fields: work, education, and welfare. In five out of 11 policy alternatives, we found a connection between behavioural variables and support for policy alternatives. While exploratory, our findings contribute innovative insights into the connection between behavioural variables and support for policy alternatives related to time use. Theoretically, the article highlights the importance of incorporating behavioural ‘signalling knowledge’ as an essential input at the policy design stage and contributes to the policy feedback literature on multidisciplinary policies.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".