A recursive measure of voting power that satisfies reasonable postulates
Bibliographic record
Abstract
The classical measures of voting power are based on players' decisiveness or full causal efficacy in vote configurations or divisions. We design an alternative, recursive measure departing from this classical approach. We motivate the measure via an axiomatic characterisation based on reasonable axioms and by offering two complementary interpretations of its meaning: first, we interpret the measure to represent, not the player's probability of being decisive in a voting structure, but its expected probability of being decisive in a uniform random walk from a vote configuration in the subset lattice (through which we represent the voting structure); and, second, we interpret it as representing a player's expected efficacy, thereby incorporating the notion of partial and not just full causal efficacy. We shore up our measure by demonstrating that it satisfies a set of postulates any reasonable voting measure should satisfy, namely, the iso-invariance, dummy, dominance, donation, minimum-power bloc, and quarrel postulates.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".