Measuring and Understanding Subjective Well-Being
Bibliographic record
Abstract
Increasing attention is being paid in academic, policy, and public arenas to subjective measures of well-being. This promising trend represents a shift towards measuring positive outcomes in psychology and greater realism in the study of economic behaviour. After a general review of past and potential uses for subjective well-being data, and a discussion of why some economists have previously been sceptical of SWB data, we present global and Canadian examples from our own research to illustrate what can be learned. Differences in subjective well-being will be shown to be large and sustained across individuals, communities, provinces and nations. Although the patterns of subjective well-being are very different across Canada than across the world, we show that in both cases the differences can be fairly well accounted for by the same set of life circumstances. Our examples of policy-relevant research findings include new accountings of the differences in individual-level SWB assessments around the world and across Canada. These highlight the importance of social factors whose role has otherwise been hard to quantify in income-equivalent terms.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".