Methods reflect values: Evaluating the shortcomings of the average for measuring population well-being.
Bibliographic record
Abstract
As governments and institutions embrace subjective well-being as a policy outcome, aggregating well-being in a population has become commonplace. The default method used to aggregate population well-being is taking the arithmetic mean (average). However, using average well-being as a key performance indicator, while useful, can omit morally relevant information, like the extent of suffering and inequality. We examine three alternative methods for aggregating life satisfaction, grounded in the ethical theories of: prioritarianism (a weighted average that prioritizes improvements at the bottom of the scale), sufficientarianism (the proportion of respondents answering above a "suffering" threshold), and egalitarianism (the degree of inequality) and compare them to the average. Toward this end, we used nationally representative data from 3,035,971 participants across 148 countries drawn from the 2005 to 2022 Gallup World Poll and the 1981-2021 World Values Survey. We found that the distribution of life satisfaction deviated significantly from a normal distribution in all countries, suggesting that using the mean and standard deviation cannot adequately capture the full distribution. After re-ranking countries according to the degree of life satisfaction inequality, we found that 56 countries deviated by at least 20 ranks compared to their average life satisfaction rankings. Finally, we observed that 9%-46% of the time, increases in average well-being at the country level were accompanied by increasing suffering and inequality. Our findings show the downside of using the average and offer alternatives that are aligned with promoting equitable well-being growth. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.163 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".