Envisioning financial disorder: financial surveillance and the securities industry
Bibliographic record
Abstract
With the emergence of increasingly digitized and electronically mediated financial markets has come a host of new technologies that seek to convert this new-found transparency into opportunities for financial monitoring and oversight. Adopting the term ‘financial surveillance’ as a descriptor for these emergent regulatory technologies, this article first develops this concept and then provides an in-depth analysis of one specific form of financial surveillance: the real-time monitoring of financial markets for breaches of trading rules through the use of sophisticated mathematical algorithms and computerized assessment tools. Based on interviews with the members of one agency, Market Regulation Services Inc., that performs this service on behalf of a number of individual marketplaces, the article examines the possibilities and limits of this surveillance technology as a mode of financial governance, and probes its larger significance as a regulatory device engaged in a particular performance of the markets.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".