Mining, climate change and Indigenous Peoples in Ontario, Canada
Bibliographic record
Abstract
In Canada, industrial developments, and resource extraction, in particular, have been responsible for much of the landscape level change within Indigenous ancestral lands. As a result, Indigenous Peoples in Canada are not only increasingly vulnerable to a changing climate, but experience synergistic, cumulative effects due to extractive industries that operate predominantly within their traditional territories (Birch, 2016; Odell et al., 2018). This chapter explores the nexus of mining and climate change within the unique context of Indigenous communities in what is presently considered Canada, focusing on the province of Ontario (Odell et al., 2018). It reveals, in particular, critical barriers to climate change adaptation that impede efforts to build community capacity and resilience, as well as highlight strategies for Indigenous communities seeking CSR. However, we found that studies exploring this relationship between climate change, mining, and Indigenous Peoples were found to be scant in the context of Ontario, despite numerous studies of these themes independently and bilaterally. This chapter seeks to initiate a discussion around the complex intersection of these three themes, while exploring the role of CSR and other mechanisms used to uphold ethical mining practice principles within the context of our review. The chapter uses a novel conceptualization to structure our exploration of the literature and emerging research need.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".