Can Corporate Monitorships Improve Corporate Compliance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the last few years, prosecutors and SEC enforcement attorneys have increasingly relied on settlement agreements (such as deferred prosecution agreements) to combat securities violations and other corporate criminal acts. Many of these agreements require the use of corporate monitors to oversee the corporation's compliance with the settlement and its implementation of a compliance program to prevent future violations of the law. Although these agreements have received significant attention from legislators and scholars, there has been no investigation into the critically important question of whether or not the use of corporate monitors achieves its intended goals. Based primarily on interviews with individuals directly involved in monitorships, we look at the entire monitorship process - including the selection of the monitor, how the monitor conducts his or her work, and what happens after a monitorship - and find that decisions at critical points during this process lead to monitorships that are significantly less ambitious than government pronouncements behind them and seem unlikely to achieve their goals on any consistent basis. After identifying these problems, we suggest measures for reform.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it