Challenges to the implementation of a sustainable development certification programme for the minerals and metals industry
Bibliographic record
Abstract
Abstract Private, voluntary eco‐labelling and certification programmes are being developed for a number of industries to promote and verify higher environmental, economic and social standards. In some cases, these initiatives have developed regardless of whether there is initial, broad support within industry. This article seeks to identify some of the challenges inherent in the implementation of a sustainable development certification programme for the minerals and metals industry. The article looks for guidance from initiatives that are drawn for the most part from other resource industries, most notably forestry. While the article takes a Canadian perspective, many of the challenges raised in the article are also applicable internationally. The article concludes that stakeholders can learn from other resource industries if a decision is made to develop a certification programme for the minerals and metals industry. Crucial to the long‐term success of such a programme will be the establishment of a true partnership among stakeholders throughout the initiative's development. Crown Copyright.
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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 | 0.001 | 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 | 0.000 |
| 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".