Bibliographic record
Abstract
As wellbeing becomes an increasingly explicit policy goal in countries across the world, the demand for evidence upon which to base intervention is growing. Featuring 41 contributing authors from 18 countries, this book surveys and synthesizes recent developments in wellbeing science and policy to highlight key lessons learned and to offer actionable insights for policy-making. Opening with a foreword by Roberta Metsola, President of the European Parliament, and an introductory chapter surveying the fundamentals of wellbeing policy, the book reviews the links between wellbeing and various domains, including income, work, health, family, altruism and empathy, ageing, gender, education, housing, environment, crime, democracy, migration, religion, digital technology, and art, culture, and creativity. The book also examines the state of the art on wellbeing policy frameworks in diverse contexts, including developed and developing countries, small and large states, across the world, documenting interventions by governmental, private, or non-governmental organizations. Case studies include Bhutan, New Zealand, Finland, the United Arab Emirates, Canada, Australia, the United Kingdom, Japan, and Malta. This book is essential reading for anyone interested in progressing towards a wellbeing economy including policy-makers, academics, and students in economics, public policy, public administration, and behavioural and political science.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".