A Public, Open, and Independently-Curated Database of Happiness Coefficients
Bibliographic record
Abstract
Abstract We present a nascent database of happiness coefficients. This is a synthesis of evidence on the size of improvements to human life experience that can be expected from changing objective, policy-amenable circumstances. The wealth of data on people’s self-reported satisfaction with life in a wide variety of circumstances, from around the world, including respondents undergoing a diversity of changes and life events and subject to a variety of public policies and policy changes, has provided a rich base of knowledge about what makes life good. This growing research literature has in recent years been met with interest from central governments looking for accountable but more human-centred approaches to measuring progress, as well as for communicating objectives, making policy, and allocating resources. Meanwhile, frameworks for benefit-cost accounting using inference from life satisfaction data have been devised. In some cases central government finance departments and treasuries are incorporating this approach into their formal methodology for budgeting. The body of causal inference about these effects is still somewhat diffuse. Collating, reviewing, and synthesizing such evidence should be led initially by academia and ultimately by a broad academic, civil society, and government collaboration. We report on the assembly of a database of summary estimates for Canada, supplemented where needed by evidence from around the world. The categorized domains of individual experience and circumstances include Education, Environment, Work, Finances, Health, Social Capital, and Crime. The paper also explains the context for and limitations of the use of a database of happiness coefficients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".