Accountability, Framing Effects, and Risk-Seeking by Elected Representatives: An Experimental Study with American Local Politicians
Bibliographic record
Abstract
Risk management underlies almost every aspect of elite politics. However, due to the difficulty of administering assessment tasks to elites, direct evidence on the risk preferences of elected politicians scarcely exists. As a result, we do not know how consistent are politicians’ risk preferences, and under what conditions they can be changed. In this paper, we conduct a survey experiment with 440 incumbent local politicians from across the United States. Using a modified version of the Asian Disease framing experiment, we show that gain/loss frames alter the stated risk preferences of elected officials. We further show that priming democratic accountability increases the tendency to engage in risky behavior, but that this shift in preference only occurs in those politicians who are interested in seeking reelection. These results inform several political science theories that assume stable risk preferences by political elites, or that make no risk assumptions whatsoever. They also provide insights into the role of political ambition and accountability in structuring the behavior of political elites.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".