The Impact of Whistleblowing Awards Programs on Corporate Governance
Bibliographic record
Abstract
Since the introduction of a whistle-blower awards program by the US Securities and Exchange Commission in 2010, securities regulators in other countries, including Canada, have adopted, or are considering adopting, similar programs. For example, in 2016, the Ontario Securities Commission adopted its own whistle-blower award program. Although the primary main reason for these programs is to encourage the reporting of securities violations to the regulator, they could also have an impact on corporate governance. This is because the implementation of such a program may prod companies to design, and then instigate, a more effective internal whistle-blowing system. A truly successful internal whistle-blowing system can enable a company to detect and correct potential wrongdoing before it causes significant harm. This article closely examines this connection between whistle-blowing award programs, companies’ compliance and risk management systems, and how a whistle-blowing award program might well result in more effective internal whistle-blowing systems without the need for a regulator to resort to the imposition of prescriptive rules. As such, this article reflects upon how whistle-blower award programs fit within new governance regulatory theory that challenges traditional “command-and-control-type”regulation in favour of an outcome-driven approach.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".