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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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; both teacher heads agree on what is shown here.
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".