Aboriginal partnerships in Canada: focus on the Diavik Diamond Mine
Bibliographic record
Abstract
Purpose In negotiation with diamond enterprises, Aboriginal communities have provided their consent for the diamond mines and have ensured their participation in all diamond projects within their traditional territories. The purpose of this study is to evaluate partnership strategies. Design/methodology/approach Interviews. Findings Natural resource enterprises support more than 650 Canadian communities and according to industry Canada this accounts for 12 percent of Canada's gross domestic product. The diamond industry accounts for 4 percent of the nation's expenditures on mining and processing and it is poised for significant growth and contribution to the economy. This emerging industry has created a unique opportunity for Aboriginal people of the Northwest Territories, also known as NWT. Practical implications Diavik's success story shows that a good mining strategy is to think beyond extraction, and to be a good corporate citizen, supporting communities and their enterprises. In 2005, the Canadian Council for Aboriginal Business awarded Diavik the gold award. As well, the Prospectors and Developers Association of Canada awarded Diavik an award for protecting its natural surroundings and for its efforts to establish and maintain good relations with local communities. Originality/value Five communities have signed partnership agreements with Diavik Diamond Mines Inc., providing joint control of training, employment and business opportunities. This paper discusses the commitments, and the progress, made by Diavik in the participation agreements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.039 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".