The supply and demand sides of corruption: Canadian extractive companies in Africa
Bibliographic record
Abstract
With the rapid expansion of Canadian investment in extractives around the world, it is perhaps not surprising that Canada’s reputation as a low-corruption country has faltered: Canada currently ranks ninth internationally in Transparency International (TI)’s corruption perception index, down from sixth in 2010, and sixth, down from first (i.e. best), in 2009 in TI’s Bribe Payers index. This article presents the preliminary findings of our ongoing research regarding both the demand side (that is, the request for bribes, principally by foreign officials) and the supply side (that is, the giving of bribes, principally by corporations) of corruption. We have examined Canadian mining companies operating in Ghana and Burkina Faso and have identified 10 “tensions” which need to be acknowledged in public policy formulation. We note that Canada is implementing policies to reduce supply-side corruption (e.g. by adopting anti-bribery legislation and guidelines for corporate social responsibility) but recommend that more be done, especially oversight of anti-corruption laws by Parliament. We also recommend that mining companies undertake ex-ante corruption risk assessment and develop proactive corporate anti-corruption policies. And, finally, while host countries have anti-corruption laws, implementation is weak. Global affairs could usefully support stronger parliamentary oversight in these countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".