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STOPPING CORPORATE WRONGS

2010· article· en· W32739624 on OpenAlexfundno aff
Peter Bowden

Bibliographic record

VenueEnvironmental Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchHealth CanadaClean Air Regulatory Agenda
KeywordsPolitical science

Abstract

fetched live from OpenAlex

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. ---------------------------------

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0450.012

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.

Opus teacher head0.175
GPT teacher head0.398
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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