Bibliographic record
Abstract
A social-evaluation functional assigns a social ranking of alternatives to each information profile in its domain. In the classical multi-profile model of social choice, profiles are restricted to welfare information: all non-welfare information is implicitly assumed to be fixed. Because of this, the conventional approach does not allow us to discern the way in which the functional makes use of non-welfare information. For that, multiple non-welfare profiles are needed. Blackorby, Bossert and Donaldson (2005a) analyze a framework in which non-welfare information may vary across information profiles. Each information profile includes a vector of individual utility functions which represent welfare information and a vector of functions which describe social and individual non-welfare information. See also Kelsey (1987) and Roberts (1980) for approaches to social choice where non-welfare information is explicitly modelled. A social-evaluation functional is welfarist if a single ordering of utility vectors, together with the utility information in a profile, is sufficient to rank all alternatives. The ordering of utility vectors is called a social-evaluation ordering. Welfarism is a consequence of three axioms: unlimited domain, Pareto indifference and binary independence of irrelevant alternatives. Unlimited domain requires that all logically possible profiles are in the domain of the functional. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".