Choosing choices: Agenda selection with uncertain issues
Bibliographic record
Abstract
This paper studies selection rules i.e. the procedures committees use to choose whether to place an issue on their agenda. The main ingredient of the model is that committee members are uncertain about their final preferences at the selection stage: they only know the probability that they will eventually prefer the proposal to the status quo at the decision stage. This probability is private information. We find that a more stringent selection rule makes the voters more conservative. Hence individual behavior reinforces the effect of the rule instead of balancing it. For a voter, conditional on being pivotal, the probability that the proposal is adopted depends on which option she eventually favors. The probability that the proposal is adopted if she eventually prefers the proposal increases at a higher rate with the selection rule than if she eventually prefers the status quo. In order to compensate for that, the voters become more selective. The decision rule has the opposite effect. We describe optimal rules when there is a fixed cost of organizing the final election.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".