Bibliographic record
Abstract
This paper provides a new explanation why several US states have implemented supermajority requirements for tax increases.We model a dynamic and stochastic OLG economy where individual preferences depend on age and change over time in a systematic way. In this setting, we show that the rst population of voters will choose a supermajority rule in order to inuence the outcomes of future elections. We explore the robustness of the basic model and also nd some empirical support for predictions derived from the model. Keywords: Supermajority, taxation, constitution, overlapping generations, political economy. JEL code: D72. Corresponding author: Mattias Polborn, Department of Economics, University of Western Ontario, London, Ontario, N6A 5C2, Canada; email: mpolborn@julian.uwo.ca . I would like to thank James Davies, Paul Klein and Matthias Messner for helpful comments; the usual disclaimer applies. 1 Introduction In thirteen US states, a supermajority of usually 2=3 of the...
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".