A Natural Experiment in Proposal Power and Electoral Success
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Does lawmaker behavior influence electoral outcomes? Observational studies cannot elucidate the effect of legislative proposals on electoral outcomes, since effects are confounded by unobserved differences in legislative and political skill. We take advantage of a unique natural experiment in the Canadian House of Commons that allows us to estimate how proposing legislation affects election outcomes. The right of noncabinet members to propose legislation is assigned by lottery. Comparing outcomes between those who were granted the right to propose and those who were not, we show that incumbents of the governing party enjoy a 2.7 percentage point bonus in vote total in the election following their winning the right to introduce a single piece of legislation, which translates to a 7% increase in the probability of winning. The causal effect results from higher likeability among constituents. These results demonstrate experimentally that what politicians do as lawmakers has a causal effect on electoral outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it