The Future of Reporting: Whistleblower 2.0
Bibliographic record
Abstract
Following our research, we conducted a roundtable with professionals from both the U.S. and Canada involved in the SEC, CFTC, as well as the OSC’s whistleblower programs including a former SEC Commissioner, Directors of the OSC, OSC members specifically from the Office of the Whistleblower, and prominent lawyers involved in this area of work. Our project outlines our research findings combined with these discussions. We begin by outlining the U.S. programs, including the SEC and CFTC programs, which date back to the enactment of the Dodd-Frank Act. Focusing specifically on the SEC’s program, whistleblowers receive awards based on information that leads to successful enforcement actions and receive protection against retaliation. There are awards specific to this program, including a 10-30% discretionary award payment of the amount collected, as well as a 30% presumption on awards that do not exceed $5 million. The OSC program, while one the best in the world, lags behind the progress made in the U.S. It was proposed that there is great potential to marry the Canadian and U.S. programs, as there is significant overlap in enforcement and in the companies at issue. However, there are notable differences between the countries that may prevent the programs from ever being fully reconcilable, including a fragmented securities regulatory framework in Canada, smaller Canadian capital markets, and cultural differences that may impact the efficacy of program incentives. With Ontario having the sole paid whistleblower program in Canada, some awards specific to this program include the 5-15% discretionary award payment of the amount collected, no presumption in place to be on the higher end, as well as a $1.5 million cap placed on awards. The paper shall provide an overview of the questions discussed during the roundtable. The questions revolved around potential promising changes to the OSC’s whistleblower program, including ideas to enhance financial incentives, retaliation provisions, use of internal compliance systems, and related actions. The OSC also discussed the importance of a whistleblower bar in Ontario and ways this could be made possible. Our paper concluded with a discussion of potential areas of future research, including discussion with behavioural science colleagues to understand how prominent a role the “classic Canadian culture” should play in the whistleblower reform, as well as collaboration with the Vancouver Anti-Corruption Institute who, in 2022, hosted a major conference on Whistleblowers and Public Integrity.
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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 | 0.032 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.039 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.018 |
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".