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Record W3167997214 · doi:10.6000/1929-4409.2021.10.113

Sociological Perspective and Legal Protection of Customary Land: Solution to Determination of Traditional Forest in Indonesia

2021· article· en· W3167997214 on OpenAlexvenueno aff
Sukirno Sukirno, Nur Adhim

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousStipulationGovernment (linguistics)Customary landLawState (computer science)ColonialismPolitical scienceLand tenureGeographyEcologyAgriculture

Abstract

fetched live from OpenAlex

This study is motivated by the perspective of government officials and the mechanism for determining customary forests which is often the cause of conflict between indigenous peoples and plantation entrepreneurs who accept concessions from the government. The aims of this study are to evaluate the legal positivism perspective that continued domein verklaring during the Dutch colonialism saw customary forests as state forests as long as they had not been determined by the government so that they could be concessioned to plantation entrepreneurs. The results showed that in order to prevent conflicts and to simplify and speed up the mechanism for determining customary forests, an idea is offered to apply the legal processing (rechtsverwerking) analogy in customary law that has been accepted by the national land law. The physical control of customary forests by customary communities by collecting products, utilizing and conserving the forest, regulated in customary law and not denied by indigenous peoples, who border, is sufficient as a sign that the customary forest is under the control of the customary community concerned. Likewise, for the stipulation mechanism, it is necessary to give authority to the local government to be able to determine customary forests.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.068
GPT teacher head0.328
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Criminology and SociologySame topicIndonesian Legal and Regulatory StudiesFrench-language works237,207