Excavating the regulatory process and risks posed by Alaska hardrock mine expansions
Bibliographic record
Abstract
Mining can cause environmental damage if managed improperly. Environmental impact assessments are designed to evaluate the risks of mining; however, these evaluations are hindered when mines expand their operational scope beyond activities considered in the original assessment. Our study focuses on the regulatory process of mine expansion and the National Environmental Policy Act (NEPA). Our multiple case study approach analyzed expansion projects for five hardrock mines in Alaska, USA. A media review identified expansion issues for each mine that received significant public attention. For each case, we examined government documents, media, and other literature to explore research questions related to variability in the application of environmental impact assessment procedures, the breadth and depth of public process, and issues raised during public comment periods. Across case studies, we synthesized the common themes and context dependence regarding the regulatory process of mining expansion. We found wide variation in the implementation of NEPA based on different institutional and geographic contexts, which led to unique sets of public concerns. We summarized differing levels of public engagement, varying approaches to cumulative effects analyses, and difficulties accessing public documents. These results demonstrate the challenges that mine expansions pose to conducting consistent and transparent environmental impact assessments.
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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 | 0.028 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".