Gradual Institutional Change in Congressional Ethics: Endogenous Pressures toward Third-Party Enforcement
Bibliographic record
Abstract
Ethics reform in Congress is expected to display extensive instability and “cycling.” Members hold conflicting views about ethics, and parties have weak capacities to order their preferences. The trend is for legislators to resist change until a scandal erupts and forces them to act. As a result, ethics reforms in Congress have typically developed through a layering of short-term and piecemeal institutional responses to the scandal of the moment. But the accumulation over time of seemingly small adjustments to the ethics process has been more path-dependent than anticipated in theories of disjointed pluralism. Legislators have had to commit to more open and collaborative forms of self-enforcement because of the feedback effects of ethics rules on Congress and the “tight-coupling” of standards of conduct between the executive and legislative branches of government. With each new scandal and partisan abuse of the process, pressures for a more independent mechanism to enforce ethics rules has grown stronger over time. The more ethics became governed by impersonal rules, the more it undermined Congress's past trajectory of political self-discipline. It is in this changing balance between positive and negative feedback effects that we can locate the mechanism that is gradually transforming ethics self-regulation in Congress into a new form of “co-regulation” with outsiders in the Office of Congressional Ethics (OCE).
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".