The Case of Stated Preferences and Social Well-Being Indices
Bibliographic record
Abstract
This paper provides a real-world test case for how to approach contemporary preference aggregation procedures. We examine the method of using stated preferences (SP) to structure social well-being indices. The method has seen increasing popularity and interest, both in economists’ laboratory research and by governments and international institutions. SP offers a sophisticated aggregation of peoples’ preferences regarding social well-being aspects and, in this way, provides elegant and non-paternalistic techniques for deciding how to weigh and prioritize various potential aspects of social well-being (health, happiness, economic growth, etc.). However, this method also poses difficulties and limitations from broader political and philosophical perspectives. This paper comprehensively charts these difficulties and suggests that SP methods should be complemented with appropriate deliberation procedures. The paper bridges the distinct perspectives of economists and political theorists in order to make SP an attractive instrument in determining policy.
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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 | 0.057 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".