The Growth of Diamond Mining in Canada and Implications for Mining Productivity
Bibliographic record
Abstract
Diamond mining in Canada began in 1998, with the first production from the Ekati mine in the Northwest Territories. Since then the Diavik mine has begun production, and two other mines are slated to begin production within two years. Canada’s share of the world value of diamond production was 15 per cent in 2003, the third largest worldwide. These mines are all located in the northern regions of Canada, and hence contribute substantially to the growth of these regions. Diamond production accounted for 19.9 per cent of total real output in the Northwest Territories in 2002, representing a phenomenal impact, especially given that the industry did not exist five years before. Given the very high level of output per hour in the diamond mining industry ?reflecting a high degree of economic rent ?and the strong expected growth of the industry in the coming years, the labour productivity growth of the overall mining industry will be favourably affected. Based on a rough simulation of the growth of the Canadian diamond mining industry in the 2001-2006 period, average annual labour productivity growth in the overall mining industry will be between one and two percentage points higher than if the diamond mining industry did not exist. Although the mining of rough diamonds is lucrative in itself, there is also much value added in the manufacture and retailing of diamond jewelry. Investment by Canadian firms in each stage of the diamond pipeline could promise large returns due to the very high value added associated with the overall diamond industry.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".