A Policy-Based Rationalization of Collective Rules: Dimensionality, Specialized Houses, and Decentralized Authority
Bibliographic record
Abstract
We offer a policy-basis for interpreting, justifying, and designing (3, 3)-political rules, a large class of collective rules analogous to those governing the selection of papers in peer-reviewed journals, where each referee chooses to accept, reject, or invite a resubmission of a paper, and an editor aggregates his own and referees’ opinions into one of these three recommendations. We prove that any such rule is a weighted multicameral rule: a policy is collectively approved at a given level if and only if it is approved by a minimal number of chambers — the dimension of the rule — where each chamber evaluates a different aspect of the policy using a weighted rule, with each evaluator’s weight or authority possibly varying across chambers depending on his area(s) of expertise. These results imply that a given rule is only suitable for evaluating finite-dimensional policies whose dimension corresponds to that of the rule, and they provide a rationale for using different rules to pass different policies even within the same organization. We further introduce the concept of compatibility with a rule and exploit its topological properties to propose a method to construct integer weights corresponding to evaluators’ possible judgments under a given rule, which are more intuitive and easier to interpret for policymakers. Our findings shed light on multicameralism in political institutions and multi-criteria group decision-making in the firm. We provide applications to peer review politics, rating systems, and real-world organizations.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".