Bibliographic record
Abstract
The individuals in the models of voting in the previous chapters disagree about the desirability of the alternatives. The individuals in the models in this chapter differ in their information about the state of nature, which determines the desirability of each alternative. Some of them know the state, and some do not. Of the informed individuals, some (partisans) prefer one of the alternatives regardless of the state, while the remainder agree on the best alternative given the state. In an equilibrium under plurality rule, all the informed individuals vote, and the uninformed individuals vote in sufficient numbers to minimize the impact of the partisans' votes. Under unanimity rule, in an equilibrium all uninformed individuals vote for the non-default outcome, leaving the decision to the informed individuals. In a model in which every individual gets a signal about the state, but these signals vary in quality, an individual votes in equilibrium under plurality rule if and only if her signal quality is high. Under unanimity rule, each individual's voting for the alternative likely to be best according to her signal is not an equilibrium, because her vote makes a difference only if every other individual votes for the non-default alternative, which means that they all received signals that that alternative is best. So if everyone else votes for the alternative best according to her signal, and the number of individuals is large, the remaining individual should vote for the non-default alternative, regardless of her signal.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".