The Role of Pilot Studies in Financial Regulation
Bibliographic record
Abstract
Abstract Financial regulators considering the desirability of a new rule or regulation sometimes use pilot studies for evidence-based decision making. Although pilot studies can generate new knowledge, they also can be expensive and subject to serious selection biases, spillover problems, and the infeasibility of a blind design. Alternatively, regulators can often evaluate a proposed regulation’s impact by analyzing archival data or applying theory based on well-accepted economic principles. We discuss why pilot studies can be useful, but also why regulators and industry participants sometimes favor pilot studies with little scientific value. We illustrate these issues by discussing various SEC pilot studies. (JEL G18, G28, G38, K22, L51)
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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 | 0.487 | 0.680 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".