Analysis of the Canadian Mining Industry’s Global Engagement Practices with Indigenous Peoples
Bibliographic record
Abstract
The aim of this research is to give a summary of how mining companies address issues of Indigenous communities from a cultural and human rights perspective. In the last 10 to 15 years, coinciding with globalization (Murphy & Arenas, 2011), increased market demands, as a result of population and economic growth, resource extraction industries have been shifting their operations from developed to developing countries (Mining Journal, November 2001, p. 353). (Murphy & Arenas, 2011). As a result, Indigenous Peoples have suffered from development on their traditional lands, specifically from the implications of development on their cultures, economies and societies. There have been growing ethical concerns about the mining industry. Environmental and human rights disasters related to the mining sector have become high profile ethical issues in many countries and contributed to growing public and media concern. The “Management In Mining Report” has cited bribery, lack of community engagement, harmful affects on agricultural land, pollution and related health hazards, as reasons for criticism. Can Corporate Social Responsibility (CSR) cause a fundamental change in protecting the rights of these vulnerable communities, and contribute to a fair distribution of costs and benefits from this large-scale resource exploitation? If so, under what conditions can this occur? This research thesis aims to determine the enablers and barriers in this process for the sake of Indigenous empowerment, as opposed to management, based on a renewed understanding of the mechanisms at play. Furthermore, the literature review will look at the role of the state in community rights and corporate responsibility, corporate attempts to justify and regulate community efforts in local development, and the ways by which civil society and NGOs can work to ensure the social and ecological sustainability of mineral extraction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".