Bibliographic record
Abstract
The need for financial market regulation is often justified by the potential for market failure arising from the information gap between managers — who possess inside information — and investors (information asymmetry), as well as from misaligned incentives (moral hazard). However, very little attention has been paid to the potential for regulatory failure. After the dot-com bubble, all market agents (directors, auditors, financial analysts, accounting standard setters) can be asked why they failed to prevent the scandals that engulfed companies such as Enron, WorldCom and Tyco. New laws in the US (Sarbanes-Oxley Act) and new Canadian independence rules indicate that the predominant view among legislators is that we need more regulation, more severe penalties, and larger enforcement budgets to protect financial markets from fraud.However, since financial markets have been regulated for the past 70 years, the same question can be asked of the regulators — why did they fail to protect investors from fraud?We could be even more impolite and ask whether too much (misguided) regulation is making financial markets more vulnerable to fraud. Regulators could just as likely be the cause of our problems as the solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.113 | 0.024 |
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".