Mining and Development: A descriptive study of the Canadian and Australian development initiatives in the countries where their mining companies operate.
Bibliographic record
Abstract
This research paper explores ways in which developed mining countries can contribute to sustainable mining development of less developed resource-rich countries. A descriptive study is made of Canada’s and Australia’s development initiatives in the countries where their mining companies operate. To do this, the paper constructs a framework of Sustainable Mining Development (SMD) against which the two country initiatives are measured. This study suggests that while Australia’ Mining for Development initiative has chosen to put a bigger emphasis on working directly with other governments to support the development of host countries’ mining sector, Canada’ Building the Canadian Advantage initiative has chosen to work more closely with Canadian mining companies and NGOs. Overall, both initiatives described in this study address certain challenges identified in the literature (section 1) and they are also aligned with the principles that define SMD. However their actions risk being too weak to have a significant impact in host countries and there are certain aspects of SMD that are ignored. While progressive capacity building of host countries’ mining industries has been expansively addressed by the Australian government (and to a limited extent by the Canadian government), there are many other challenges faced by developing mining countries that could be better addressed by both initiatives such as: promoting more revenue transparency and accountability in the industry, supporting host countries’ understanding of the costs and benefits of mining activities and, assisting in the development of mechanisms that ensure thorough compliance of mining regulations and monitoring of commitments and activities by foreign mining companies.
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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 | 0.004 | 0.012 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".