Digging for accountability in Canada
Bibliographic record
Abstract
Canada is a prominent leader in the global extractive sector, with more than 800 Canadian mining corporations active in over 100 countries across the globe. Canadian mining assets overseas are valued at $144.2 billion, accounting for approximately 65% of the nation’s total mining assets. However, Canada’s dominance in the international mining industry has come at a cost, especially for the Global South. Historically, Canadian mining corporations have been under scrutiny for taking advantage of weak legal systems in underdeveloped nations. The public has become increasingly aware of alleged human rights abuses and socio-environmental disasters involving Canadian mining operations overseas. Despite these behaviours, there remains an absence in global regulatory treaties litigating corporate accountability in the extractive industry. Liabilities from mining externalities have consequently been ignored through non-binding international frameworks, national policies, and CSR. However, the legitimacy of global frameworks and CSR practice have been called into question as socio-environmental negligence remains unabated across the Global South’s extractive sector. This chapter reviews the international legal systems, national policies, and CSR mechanisms regulating the Canadian mining industry in the Global South. It specifically addresses gaps in knowledge related to Canadian foreign ownership and CSR practice in underdeveloped nations, exploring the impact of Toronto Ventures Incorporated within the Philippines as a case study for analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".