Challenges And Prospects Of National Legal Reform In Promoting The Digitalization Of Coal Mining Through Artificial Intelligence
Bibliographic record
Abstract
The digitalization of the coal mining sector through the adoption of Artificial Intelligence (AI) technology represents a strategic step toward enhancing efficiency, transparency, and sustainability in the extractive industry. However, the implementation of AI in this sector still faces complex legal challenges, including regulatory gaps, weak institutional coordination, and the absence of national technical standards. This study aims to examine the challenges and prospects of national legal reform in supporting the digital transformation of coal mining through a normative juridical approach. The method employed is library research with descriptive qualitative analysis and comparative studies of regulations in other countries such as Australia and Canada. The findings indicate the necessity for regulatory revision, the establishment of AI standards, and multi-stakeholder collaboration as key factors for the success of national legal reform in the mining digitalization era. This study recommends accelerating the formulation of AI regulations in the mining sector as part of the national digital transformation strategy.
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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".