Bibliographic record
Abstract
Part A In this chapter, we present the most commonly used anonymous social-evaluation orderings for fixed populations. Many of the social-evaluation orderings discussed in this chapter have been proposed or investigated in the literature on the measurement of income inequality. We have modified those orderings so that they rank vectors of well-being. Each of the modified orderings can be used to define a welfarist principle for social evaluation. Although we focus mainly on the properties of social-evaluation orderings, we also investigate the relationship between these orderings and indexes of utility inequality. We show that all social-evaluation orderings that satisfy a few basic assumptions provide a trade-off between average utility and inequality as measured by an index. In addition, we show that orderings of average-utility inequality pairs, where inequality is measured by an index applied to utility levels, are equivalent to social-evaluation orderings. The utilitarian ordering is insensitive to inequality of well-being (but not to inequality of income or consumption) and, for that reason, it has been rejected by some. There are several classes of inequality-averse social-evaluation orderings, however, that give priority to the interests of people with low levels of wellbeing. Some members of the generalized utilitarian and generalized Gini classes as well as the maximin and leximin (lexicographic maximin) orderings have this property.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".