Bibliographic record
Abstract
This book provides a comprehensive analysis of the foreign bribery laws, and related laws and regulations, in all of the major common law jurisdictions. For Australia, Canada, Ireland, New Zealand, South Africa, the United Kingdom, and the United States, the critical factors associated with the laws that are applied to bribery in foreign settings are explained and analyzed. Given the extent of their extraterritorial reach, the Foreign Corrupt Practices Act (“FCPA”), the UK Bribery Act, and the official guidance associated with each are extensively addressed along with the related legal obligations related to record-keeping practices and maintaining adequate internal controls and effective compliance programs. Within the same framework, the foreign bribery legislation in the other major common law jurisdictions—Australia, Canada, Ireland, New Zealand, and South Africa—are similarly addressed. For each jurisdiction, careful attention is given to laws that may expose an individual or entity to private or commercial bribery in foreign settings as well as to the application of laws relating to money laundering and accounting and record-keeping practices to situations involving foreign bribery. Throughout, special attention is given to explaining the criteria used in each jurisdiction to establish liability on the part of an entity or organization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".