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Record W4206396741 · doi:10.54849/monas.v3i2.89

Coal mine management in East Kalimantan: a review of public policy

2021· review· en· W4206396741 on OpenAlexaff
Devi Triady, Dewi Saraswati

Bibliographic record

VenueMonas Jurnal Inovasi Aparatur · 2021
Typereview
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCoal miningLaw enforcementCorporate governanceTransparency (behavior)BusinessContext (archaeology)CoalEnvironmental planningEnvironmental resource managementEnforcementNatural resourcePolitical scienceLawEnvironmental scienceEngineeringGeographyWaste managementFinance

Abstract

fetched live from OpenAlex

The problems posed by coal mining in East Kalimantan have an environmental impact and an imbalance of economic growth with social development. In practice, mine management that does not meet the principles of good management, such as corrupt practices, uncontrolled mining permits, indicates the need for a particular study related to coal mining policies and regulations in East Kalimantan Province. Therefore, this paper is intended to analyze aspects of coal mining policy and regulation as well as policy implications to improve coal mining governance, especially in the perspective of preventing corruption in coal mining management in East Kalimantan. This research uses the desk study method with descriptive analysis of related literature related to coal mining management, especially in East Kalimantan. The results of this study indicate that the governance of coal mining in East Kalimantan needs to be improved in the context of Law Number 3 of 2020 concerning Amendments to Law Number 4 of 2009 concerning Mineral and Coal Mining through the application of natural resource management principles as well as the application of the concept of governance. , the need to improve policies and governance of coal mines (licensing, transparency, and law enforcement) and the need to strengthen the institutional system in the management of this mine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.312
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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