Bibliographic record
Abstract
Abstract. Legislative scholars have paid almost no attention to explanations for the level of compensation provided to legislators, either within a country or cross-nationally, despite its importance to members and institutions. I posit a simple theory based on state wealth to explain differences in legislative pay. I test this theory using two novel data sets, one on 35 national assemblies, the other on subnational assemblies in Australia, Canada, Germany and the United States. Analysis of these data reveals that national or state wealth is strongly associated with legislator compensation. This finding is consistent with an intriguing analog in the labour economics literature. Résumé. Les érudits du monde législatif ne se sont guère penchés sur les raisons des divers niveaux de rémunération des législateurs, à l'échelle nationale ou transnationale, malgré l'importance du sujet pour les institutions et les membres des législatures. Pour expliquer cette disparité, j'avance une simple théorie fondée sur la richesse des États. J'évalue ensuite cette théorie en m'appuyant sur deux nouvelles bases de données, la première portant sur 35 assemblées nationales et l'autre sur des assemblées sous-nationales en Australie, au Canada, en Allemagne et aux États-Unis. Ces analyses statistiques démontrent qu'il existe effectivement un lien étroit entre la richesse de l'État et la rémunération des législateurs. Cette constatation est confirmée par une analogie fascinante dans la littérature sur l'économique du travail.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".