Well-Being for Public Policy
Bibliographic record
Abstract
Abstract The case is made for implementing national accounts of well-being to help policy makers and individuals make better decisions. Well-being is defined as people's evaluations of their lives, including concepts such as life satisfaction and happiness, and is similar to the concept of “utility” in economics. Measures of well-being in organizations, states, and nations can provide people with useful information. Importantly, accounts of well-being can help decision makers in business and government formulate better policies and regulations in order to enhance societal quality of life. Decision makers seek to implement policies and regulations that increase the quality of life, and the well-being measures are one useful way to assess the impact of policies as well as to inform debates about potential policies that address specific current societal issues. This book reviews the limitations of information gained from economic and social indicators, and shows how the well-being measures complement this information. Examples of using well-being for policy are given in four areas: health, the environment, work and the economy, and social life. Within each of these areas, examples are described of issues where well-being measures can provide policy-relevant information. Common objections to using the well-being measures for policy purposes are refuted. The well-being measures that are in place throughout the world are reviewed, and future steps in extending these surveys are described. Well-being measures can complement existing economic and social indicators, and are not designed to replace them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".