Quebec's Mining Policy Performance: Greater Uncertainty and Lost Advantage
Bibliographic record
Abstract
The mining sector has played a significant part in the growth and development of Canada and Quebec. Mining exploration and extraction contributes to Quebec’s economy and creates high paying employment in remote and rural areas. It supports jobs in ore processing and contributes significantly to Quebec’s domestic exports. Yet this sector is currently facing numerous challenges that are threatening future exploration and mine development in the province. Economic challenges include rising input prices, difficulty securing investment financing for exploration, sluggish economies, and increasingly risk-averse investors. In addition to these cyclical challenges, Quebec is also facing deterioration in the attractiveness of its policy environment for mining. Since 2009, Quebec has introduced a number of policy changes and initiatives. The effects of such continually changing policies has been to increase uncertainty for mining and exploration companies in Quebec, with the result being an increase in the percentage of companies deterred from investing in the province. Quebec has changed the policy environment that made it a top-ranked jurisdiction for mining investment, and the results of the Fraser Institute Survey of Mining Companies clearly show its declining attractiveness to mining investment since 2009/10. From 2007/08 to 2009/10 Quebec was ranked as the most attractive jurisdiction for mining investment in the world. In the most recent 2012/13 survey it had fallen to the 11th most attractive. An analysis of the policy factors evaluated in the survey shows that recent policy changes have had varying effects on deterring mining investment, with four factors responsible for nearly half of the investment strongly deterred in the 2012/13 survey. A review of these shows how recent policy changes may have contributed to the observed increase in investment deterred. These factors are reviewed, and recommendations are made for each.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".