Symposium on Revenue Transparency, Resource Development, and the Challenge of Corruption
Bibliographic record
Abstract
Revenue transparency and corruption in the mining industry have long been topics of national and international conversation. Mining plays an important role in the Canadian economy, contributing billions to Canada’s GDP. It is also the only domestic industry in which Canada plays an undisputed leading international role, having major operations in countries around the world. Unfortunately, there is also a dark side to mining. Historically, both in Canada and worldwide, very few local communities, Indigenous peoples, or developing and underdeveloped nations have benefitted from mining development. To the contrary, these communities have typically borne heavy costs associated with mining activities and reaped few long-term benefits. Further, mining in underdeveloped countries with poorly enforced governance and transparency laws presents multiple opportunities for corruption and social unrest. In response, ethically responsible and sustainable mining have become fundamental objectives for leading Canadian mining companies, mining associations, and governments. This symposium aimed to spark an international dialogue on the Extractive Industries Transparency Initiative, its implementation, its effectiveness, and areas for improvement in promoting revenue transparency and mitigating corruption. As part of its ongoing “Ethics and Mining” related research, CBERN used the workshop to convene a series of meetings and public lectures to assess progress to date on meeting the challenges posed by corruption for resource extraction and to map a ‘next steps’ research agenda. Invited speakers and participants came from Canada, Africa, and the United Kingdom.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".