Bibliographic record
Abstract
In a model of electoral competition in which a position is a distribution of wealth, the citizens have preferences over the candidates about which the candidates are uncertain, each citizen's payoff function over wealth is strictly concave, and a Nash equilibrium exists, the candidates propose the same distribution. In simple examples, swing voters and citizens whose votes are more likely to be pivotal are assigned more wealth. If individuals differ in their earning power, can choose their hours of work, and care about both their consumption and their hours of work, under some conditions the collective choice problem in which the alternatives are the individuals' favorite tax-subsidy schemes has a Condorcet winner, which is the favorite scheme of the individual with median earning power. In a variant of the model in which which the alternatives are finitely many linear tax-subsidy schemes and the individuals are ordered by pre-tax income independently of the tax-subsidy scheme, the favorite alternative of median individual is a strict Condorcet winner. In a model in which the tax-subsidy scheme is the outcome of society-wide bargaining in which any majority can expropriate the complementary minority and any minority can withhold its resources, a tax-subsidy scheme in which the tax rate is 50% and tax revenue is shared equally is the outcome of both the Shapley value and the dissatisfaction-minimizing distribution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".