Bibliographic record
Abstract
The corporate meltdowns of this and the previous decade in the US - WorldCom, Enron, Tyco, and in Australia - FAI, HIH and AWB being among the many examples - have resulted in the governments of those two countries introducing legislation and policy guidelines aimed at minimising future corporate misbehaviour. The US has introduced the Sarbanes Oxley Act, with requirements on corporate accountants and auditors, as well as its whistleblowing provisions. It has revised the Federal Sentencing Guidelines for Organizations. New rules for the NYSE and NASDAQ have also been introduced. In addition, the U.S. Securities and Exchange Commission and U.S. Department of Justice have further strengthened the Foreign Corrupt Practices Act 1977, last revised in 1997. Australia has revised the Corporations Act to include whistleblower protection clauses as well as adopted the ASX Corporate Governance guidelines. Standards Australia has issued its handbook on corporate governance. Although not a business issue, the Australian government has also announced that it will introduce whistleblower protection legislation for the public sector by the end of its first term in office. This legislation will likely influence whatever the private sector does in this respect. The inclusion of whistleblower protection in both Sarbanes–Oxley and the Corporations Act reflects a growing body of research that finds that people internal to an organisation are the most effective way to identify corporate wrongs. The Commonwealth Treasury has recently issued an options paper, stating that the Corporations Act has been ineffectual. It has sought submissions on revisions to the legislation that the Australian Government could take. This paper examines the policies advocated by over 20 organisations and academics concerned with whistleblowing issues. It draws the conclusion that on current indications, even after further reform, Australian legislation and policies are still likely to be less effectual than overseas practices. ---------------------------------
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".