Social License, Mine Closure, and the Exploration Geologist
Bibliographic record
Abstract
Abstract This paper describes the potential value that can be gained when exploration geologists, early in the project life cycle, contribute to aspects of project development that have historically been outside of their purview. Increasingly strong societal pressures are being directed toward exploration and mining to ensure (1) an enduring social license to operate is in place and (2) closure and postclosure mine liabilities are fully addressed and funded. These actions are consistent with the professional and ethical obligation of caring for human and ecological well-being over both the short and long term. The roles and responsibilities of exploration geologists are evolving as a result. A singular focus on searching for and assessing the nature of ore deposits from a technical perspective has expanded to include (1) contributing to building the foundation for a social license to operate and (2) capturing increasing amounts of data and information relevant to the full mine life cycle, up to and including the closure and postclosure phases of activity. These activities are additional to the traditional geologic role of the exploration geologist and will further enhance the value of an exploration project, despite the rare transition of an exploration target to an operating mine. This expanded role is critical for aligning industry and social values, strengthening trust in the mining industry, and enhancing the appeal of the mining industry as a desirable career option.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".