Addressing the Corruption of Foreign Public Officials: Developments and Challenges within the Canadian Legal Landscape
Bibliographic record
Abstract
Abstract With 2018 marking the twentieth anniversary of Canada’s Corruption of Foreign Public Officials Act, an opportunity presents itself to take stock of both developments and challenges for the legislative scheme. As demonstrated by a review of the parliamentary record, the desire to enact legislation to criminalize the offering of an inducement to a foreign public official to secure a business advantage was decidedly international in nature, with Canada aiming to bolster the efforts of others to create a level playing field for companies operating abroad. Yet, despite good intentions, as well as amendments to strengthen the Act in 2013 and the passage of additional transparency obligations in 2014, Canada’s legislative scheme has not kept pace with the international and multi-jurisdictional realities of the problem to be addressed. Renewed interest needs to be paid to the demand side of a foreign bribery transaction. In addition, the confiscation or forfeiture of any ill-gotten gains must become a priority, with the touting of success in securing the voluntary payment of sizeable fines failing to provide for a sufficient accounting for the wrongs done, particularly if the victims of corruption, even as a class that needs clearer definition, are to be made a true concern of the Act. The challenges posed by matters of immunity and the need to improve matters of multi-jurisdictional cooperation also need further attention.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.028 | 0.017 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".