Pivots or Partisans? Proposal-Making Strategy and Status Quo Selection in Congress
Bibliographic record
Abstract
Lawmakers vary considerably in how effectively they advance their priorities through Congress. However, the actual proposal-writing strategies undergirding these differences have remained largely unexplored, due to measurement and methodological difficulties. These obstacles have included prohibitively small sample sizes, costly data requirements, and strong theoretical assumptions. In this paper, we address these obstacles and analyze the proposal strategies of effective lawmakers directly, using original measures of the spatial locations of congressional bill proposals and associated status quos generated by jointly scaling cosponsorship, roll-call, and interest group position-taking data for 1,007 bills from the 110th through 114th Congresses. Because interest groups take positions on bills before they receive votes, our measures cover many bills that die in committee, permitting comparisons between successful and unsuccessful bills. We demonstrate that legislative advancement favors moderate proposals over partisan ones, and that effective lawmakers are those who make proposals closer to the median even at the expense of their preferred policy.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".