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MEKANISME AKSES PADA HAK KEPEMILIKAN DI KESATUAN PENGELOLAAN HUTAN PRODUKSI MERANTI, SUMATERA SELATAN

2017· article· id· W2754747631 on OpenAlexaff
Ja Posman Napitu, Aceng Hidayat, Sambas Basuni, Sofyan Sjaf

Bibliographic record

VenueJurnal Penelitian Sosial dan Ekonomi Kehutanan · 2017
Typearticle
Languageid
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

The interest of various parties on forest utilization access lead to the ambiguity of property rights due to user overlapping. This research explained the ambiguity factors of property rights from access mechanism and its relation to the land conflict. The research using purposive sampling method to obtain data of land use change, documents, historical study, as well as in-depth interviews of 123 people key informant. Rapid Land Tenure Assessment (RaTA) and descriptive analysis method were used to analyze the data. The results showed that both access and property rights theory could explain the overlapping use on forest area in Meranti Forest Management Unit (FMU). Analysis of rights-based access mechanism explained factors within the property rights status and the causes of land overlapping, i.e. the dynamics of management change, boundaries area issues, and lack of control. The factors of land user based on structure mechanism were the kinship ties, patroness system or pesirah, community and religious leaders. The access of structure mechanism have lead to claim of 38.53% areas of Meranti FMU. Changes of the rules have increased new users and causing overlapping between bussines license holder with community access. The research recommends avoiding change of area management forms, and for involving local communities in determining new users, duration, and profit sharing.

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), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0070.002
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.229
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations10
Published2017
Admission routes1
Has abstractyes

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