Bibliographic record
Abstract
This article studies the role of subpoena power in enabling policymakers to make better-informed decisions. In particular, we take into account the effect of subpoena power on the information voluntarily supplied by interest groups as well as the information obtained by the policymaker via the subpoena process. To this end, we develop a model of informational lobbying in which interest groups seek access to the policymaker in order to provide him verifiable evidence about the desirability of implementing reforms they care about. The policymaker is access-constrained, that is, he lacks time/resources to scrutinize the evidence owned by all interest groups. The policymaker may also be agenda-constrained, that is, he may lack time/resources to reform all issues. We find that if a policymaker is agenda-constrained, then he is better off by having subpoena power. On the other hand, if a policymaker is not agenda-constrained, he can be worse off by having subpoena power. The key insight behind these findings is that subpoena power, while it increases the policymaker’s ability to acquire information from interest groups, it also alters the amount of information they voluntarily provide via lobbying, and that the net effect differs depending on whether or not the policymaker is agenda-constrained.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".