Predicting majority rule: Evaluating the uncovered set and the strong point
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper compares two solution concepts for majority rule decision-making in multi-dimensional settings: the uncovered set and the strong point. Our goal is to determine which of these solution concepts is the appropriate generalization of the median voter theorem to more complex (and more realistic) multi-dimensional majority-rule settings. By making this comparison, we also contribute to the debate about the degree of sophisticated decision-making exhibited by experimental subjects and their real-world counterparts. Using data from eleven previously-published majority rule experiments and analytic techniques drawn from geography, our analysis confirms expectations that the uncovered set provides accurate predictions of majority-rule decision-making; and, moreover, that the strong point provides little added insight, either as a solution concept on its own, or as a predictor of where outcomes lie inside the uncovered set.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it