Market structure and disempowering regulatory intermediaries: Insights from U.S. trade surveillance
Bibliographic record
Abstract
Abstract Public agencies outsource a wide variety of tasks to nonstate actors, or what can be referred to as regulatory intermediaries. In certain circumstances, these agencies may seek to disempower those regulatory intermediaries by reclaiming, duplicating, or transferring the outsourced task. When will these disempowerment attempts be successful? This article presents the Market Structure Hypothesis, which contends that the level of competition between regulatory intermediaries will, all things equal, determine whether disempowerment attempts succeed. To test this hypothesis, this article examines the U.S. Securities and Exchange Commission's attempts to acquire the independent capacity to conduct nationwide trade surveillance in the 1980s (Market Oversight Surveillance System) and 2010s (Consolidated Audit Trail). Evidence derives from archival materials, a Freedom of Information Act Request, and 60 interviews in Oxford, London, Toronto, New York City, and Washington, DC. The empirical results corroborate the hypothesis' expectations, contributing to our understanding of public‐private partnerships and shedding new empirical light on an understudied topic of securities regulation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".