MétaCan
Menu
Back to cohort
Record W2026344022 · doi:10.5539/jsd.v5n7p62

Local Struggle for Accessing State Forest Property in a Montane Forest Village in Java, Indonesia

2012· article· en· W2026344022 on OpenAlexvenueno aff
Ahmad Maryudi, Max Krott

Bibliographic record

VenueJournal of Sustainable Development · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersDirektorat Jenderal Pendidikan TinggiUniversitas Gadjah MadaDeutsche Forschungsgemeinschaft
KeywordsJavaState (computer science)Forest managementBusinessProperty rightsResource (disambiguation)Community forestryState forestMonopolyForestryEnvironmental resource managementGeographyNatural resource economicsPolitical scienceEconomicsLawMarket economy

Abstract

fetched live from OpenAlex

How local people can access state forests has become a central issue in forest resource management in Indonesia in recent years. This is because for most of the ‘modern history’ of forest management in the country, the forest resources have been at the monopoly of the state. In fact, there have been an increasing number of local people’ struggles for obtaining meaningful and legal access to the state forest resources in the country. In response to these, the forest administration has implemented a community forestry program. This paper aims to observe the transformation the people’s access to the forests, whether the community forestry program improve the access to the state forest resources. Employing the theory of access provided by Ribot and Peluso (2003), which defines access as the ability to benefit from a resource either legal or illegal, this paper finds that the community forestry program actually reduces the people’s access to the 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations35
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicConservation, Biodiversity, and Resource ManagementFrench-language works237,207