Testing for Welfare Comparisons when Populations Differ in Size
Bibliographic record
Abstract
Assessments of social welfare do not usually take into account population sizes. This can lead to serious social evaluation flaws, particularly in contexts in which policies can affect demographic growth. We develop in this paper a little-known though ethically attractive approach to correcting the flaws of traditional welfare analysis, an approach that is population-size sensitive and that is based on critical-level generalized utilitarianism (CLGU). Traditional CLGU is extended by considering arbitrary orders of welfare dominance and ranges of “poverty lines” and values for the “critical level” of how much a life must be minimally worth to contribute to social welfare. Simulation experiments briefly explore the normative relationship between population sizes and critical levels. We apply the methods to household level data to rank Canada’s social welfare across 1976, 1986, 1996 and 2006 and to estimate normatively and statistically robust lower and upper bounds of critical levels over which these rankings can be made. The results show dominance of recent years over earlier ones, except when comparing 1986 and 1996. In general, therefore, we conclude that Canada’s social welfare has increased over the last 35 years in spite (or because) of a substantial increase in population size.
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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 | 0.099 | 0.397 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".