Whose Ear to Bend? Information Sources and Venue Choice in Policy-Making
Bibliographic record
Abstract
Important conceptualizations of both interest groups and bureaucratic agencies suggest that these institutions provide legislatures with greater information for use in policy-making. Yet little is known about how these information sources interact in the policy process as a whole. In this paper we consider this issue analytically, and develop a model of policy-making in whichmultiple sources of information – from the bureaucracy, an interest group, or a legislature’s own in-house development – can be brought to bear on policy. Lobbyists begin this process by selecting a venue – Congress or a standing bureaucracy – in which to press for a policy change. The main findings of the paper are that self-selection of lobbyists into different policy-making venues can be informative per se, and that this self-selection can make legislatures prefer delegation to ideologically distinct bureaucratic agents over ideologically close ones. Changes within the FederalTrade Commission during the 1970s are reinterpreted in the context of our model.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".