Sustainability and Public Engagement in Mining: The Role of Engineers
Bibliographic record
Abstract
Social issues are increasingly recognized as significant inhibitors to mineral development projects. Increasingly, social risk is being recognized as a key factor determining the success of a mineral investment. Groups opposed to a mine for social or political reasons often use environmental impacts, real or perceived, to prevent mine development. These risk factors depend largely on cultural perceptions of mining activities and must be understood as such in order to be appropriately managed. A first step to addressing social issues is inclusive, transparent and meaningful engagement of stakeholders. This process allows stakeholders to understand what the other parties value in order to collectively establish a common currency for development and the creation of mutual value. Expanding the scope of benefits and values a mine can bring is of increasing importance to mining companies who typically consult outside specialists remote from the mine site and late in the development timeline for this purpose. Training technical staff, engineers and geologists, who make initial and ongoing contact with local interests, in a holistic approach to mine development is crucial to successful and economic mineral development projects. Further extending this conversation to the general public, media governments and non-governmental organizations is a necessary step in developing a meaningful discourse on the benefit of mining activities.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".