Bibliographic record
Abstract
This chapter reviews developments in wellbeing in recent years, spanning international and national policy efforts and academic research. It briefly reviews the state of play on wellbeing definition and measurement, the determinants of wellbeing, and insights on the design of policy and interventions for wellbeing, noting the need for a survey of policy interventions to elicit the lessons learned. The chapter provides an overview of the key insights on income, work, health, family, altruism and empathy, age, gender, and education (included in Part I of the book); on housing, environment, crime, democracy, migration, religion, digital technology and art, culture and creativity (included in Part II ); and on the experiences of Bhutan, New Zealand, Finland, the United Arab Emirates, Canada, Australia, the United Kingdom, Japan, and Malta (included in Part III of the book). Together, these chapters offer evidence of the diverse factors that impact human wellbeing, of how public policy can influence these factors, as well as a diverse range of wellbeing policy experiences on a country-by-country basis. It concludes with an appeal to consider the actionable points resulting from the volume. The chapter is supported by an Appendix of cheat-sheets for policy-makers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".