Uncertainty and Incommensurabilities
Bibliographic record
Abstract
Part A In this chapter, we investigate several variations on our basic model. First, we incorporate uncertainty and extend our characterizations of the critical-level utilitarian (CLU) and number-sensitive critical-level utilitarian (NCLU) classes of population principles. The resulting classes are called ex-ante CLU and ex-ante NCLU. Our theorems are variants and extensions of Harsanyi's (1955, 1977) well-known social-aggregation theorem. Additional axioms are used to extend fixed-population principles to the variable-population environment. Although Harsanyi used a single-profile setting, we investigate the multi-profile case but note that the single-profile approach could be extended to the variable population model as well. Second, we examine incommensurabilities in social rankings by employing social-decision functionals, which associate a quasi-ordering (rather than an ordering) on the set of alternatives with each utility profile. Several new axioms are used to characterize the critical-band generalized utilitarian class of principles. The band is an interval and, according to those principles, one alternative is at least as good as another if and only if it is at least as good according to critical-level generalized utilitarianism for all critical levels in the band. UNCERTAINTY The principles presented and discussed in Chapters 3 to 6 can be used to rank actions or combinations of institutional arrangements (including legal and educational ones), customs, and moral rules, taking account of the constraints of history and human nature. If each of these leads with certainty to a particular social alternative, they can be ranked with any welfarist principle. This approach does not make a strong distinction between ends and means: actions are embedded in the associated alternatives.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".