Using decision science to build trust in mine closure decisions
Bibliographic record
Abstract
In today’s changing social and environmental landscape, society requires organizations to shift to an everevolving world of ‘tell me what you are doing’, through to ‘show me what impact you are having’, and now to ‘involve me in your work’. With public trust in mining at an all-time low globally (Dhawan 2023), decision transparency involving competing objectives and unavoidable trade-offs can help build trust with interest groups, titleholders, and regulators in mine closure decisions. Decision science offers a structured approach to integrate and weigh multiple perspectives on objectives, risks, trade-offs, and preferences, that supports efficient mine closure planning. Breaking complex decisions down using logical frameworks with a structured and transparent approach helps people gain a common understanding, so that they can identify and discuss objectives. It exposes options across competing objectives at the core of difficult decisions. In contrast to other approaches such as gut feel and ‘we’ve always done it this way’, it addresses multiple objective trade-offs directly. It is a conceptually intuitive and easily applied approach. An intentional shift to structured, logical thinking provides confidence and clarity resulting in higher efficiency projects, cost savings, and a significant reduction in re-work. A decision science approach includes: (1) front end facilitation to frame the decision or decision series, (2) assessment of the project objectives including potentially conflicting desires of internal or external interest groups, (3) divergent creative thinking to identify new alternatives that better fulfill the prioritized objectives, (4) qualitative and quantitative analysis to assess and contrast alternatives based on how well they fulfill desired objectives, (5) threat identification and uncertainty management aspects that will flow into the project management and execution phase of the closure. A healthy decision culture, where teams and decision-makers foster a culture of inquiry instead of advocacy allows people embrace creative conflict and curiosity around differing values and objective trade-offs. This results in a shared understanding, identifies superior alternatives, reduces risk, and accelerates project development. Our industry can benefit from adopting a decision science approach to the many important, complex decisions we all face, as we work to increase efficiency, reduce cost, and build trust in mine closure decisions.
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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 | 0.001 | 0.002 |
| 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".