‘Pretaliatory’ Enforcement Action for Chilling Whistleblowing through Corporate Agreements: Lessons from North America
Bibliographic record
Abstract
Abstract Studies have shown that potential whistleblowers are reluctant to report misconduct because they fear retaliation. In Australia, fear of retaliation is exacerbated for private-sector employees where the lack of prescriptive legislation aggravates vulnerability in all but exceptional circumstances. Through examining the codes of conduct of Australia's 100 largest listed companies (‘Codes’) this article argues that while Codes have the potential to provide an important regulatory function through facilitating whistleblowing, the breadth of confidentiality undertakings contained therein may instead be chilling potential whistleblowers from speaking up. While companies have legitimate interests in protecting confidential information, it is well-established that employees may disclose their employer's unlawful conduct to the government, even if such disclosure is in violation of the company's confidentiality policy. To affirm this right, in the United States (US), federal regulators have recently taken ‘pretaliatory’ enforcement action against companies for requiring employees to execute confidentiality agreements that stifle the reporting of possible violations of federal laws. Such regulation by enforcement has successfully effected cultural change through facilitating widespread amendments to US corporate confidentiality agreements. Accordingly, this article argues that any future Australian legislation should include an ‘anti-confidentiality provision’ similar to the US and Canadian frameworks to affirm an employee's right to communicate with a regulator directly, despite any purported agreement or corporate policy to the contrary.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".