Ecological Impacts and Socio-Legal Infrastructure as an Approach to Environmental Management in Ex-Mining Land Reclamation
Bibliographic record
Abstract
Mining, along with plantations, is one of the main economic backbones of the Indonesian provinces of Kalimantan. The main concern of extractive economics is deforestation and environmental damage that threatens natural sustainability. Most of the previous research focused on the issue of environmental sustainability in the industrial context and regional spatial planning. To fill this void, this study originally aims to analyze how local wisdom is useful in managing ex-mining reclamation practices. This research was conducted in Margahayu, Kutai Kertanegara, East Kalimantan Province. The method used in this research is empirical legal research by adopting a data-based approach. The results show that ex-mining reclamation in Margahayu aims to restore the land use according to its function and is beneficial for agriculture and small-scale plantations. The findings underline that the participation of local communities is very useful in restoring the function of the ex-mining land, due to their interest in rehabilitating spatial planning and ecological supports that are useful for their livelihoods. In this context, this finding requires inclusion of local interest-based participation as an important social infrastructure in reforestation and mine land reclamation in Kalimantan.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".