Building an Epidemiology of Happiness
Bibliographic record
Abstract
Starting from the assumption that improving well-being is the central consideration for public policies, we show how subjective well-being research can help, and already is helping, to choose public policies based on their consequences for all aspects of life.The core of the paper lies in examples where the methods we propose, often in systematic experimental contexts, have already been used to guide the evaluation and ranking of alternative policy options in public health, education, workplace training, and social welfare.The arrival of COVID-19 has increased the urgency for a well-being focus, since the policy decisions being faced by governments dealing with the pandemic require an approach much broader than provided by more typical policy evaluations in all disciplines, including especially the social context and the distribution of costs and consequences.A broader approach to policy design and choice is fully consistent with the underlying aims of epidemiology, with similar gains likely in other policy disciplines.A focus on subjective well-being as an umbrella measure of welfare might help to restore to economics the breadth of purpose and methods it had two centuries ago, when happiness was considered the appropriate goal for private actions and public policies.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".