Polarized Contributions but Convergent Agendas
Bibliographic record
Abstract
The political process in the United States appears to be highly polarized: Data show that the political positions of legislators have diverged substantially, while the largest campaign contributions come from the most extreme donor groups and are directed to the most extreme candidates. Is the rise in campaign contributions the cause of the growing political polarization? In this paper, we show that, in standard models of campaign contributions and electoral competition, a free-rider problem among potential contributors leads naturally to polarization of campaign contributors but without any polarization in candidates' policy positions. However, we go on to show that a modest departure from standard assumptions -allowing candidates to directly value campaign contributions (because of "ego rents" or because lax auditing allows them to misappropriate some of these funds) -delivers the ability of campaign contributions to cause policy divergence. Consistent with the model, we document that a candidate's share of contributions in U.S. House of Representatives races is higher when her opponent's agenda is more extreme.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".