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Record W3210810990 · doi:10.18280/ijdne.160511

Can Forest Management Units Improve Community Access to the Forest?

2021· article· en· W3210810990 on OpenAlexvenueno aff
Golar Golar, Hasriani Muis, Sudirman Dg Massiri, Abdul Rahman, Arman Maiwa, Fardhal Pratama, Rhamdhani Fitrah Baharuddin, Wahyu Syahputra Simorangkir

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersUniversitas Tadulako
KeywordsForest managementCommunity forestryGeneral partnershipDeforestation (computer science)BusinessGovernment (linguistics)ForestryUnit (ring theory)Resource management (computing)Environmental resource managementEnvironmental economicsGeographyEconomicsFinance

Abstract

fetched live from OpenAlex

This paper examines the Forest Management Unit's (FMU) role in enhancing access to forest area utilization, especially in production and protected community-based forests, to suppress the rate of deforestation. We research five FMU in central Sulawesi. The analysis method is qualitative based on emic information from FMU, community, academic, local government, and direct field observations. This paper explains that public access in forest resource utilization is a deciding factor in helping the community face the impact of economic crises. To make it happen, the primary role of FMU is necessary. FMU Should be increasing public access to the state-owned forest by optimizing the facilitating functions. Providing investment opportunities for forest management based on the community in partnership schemes can realize a broad impact and national issues on empowering forest communities. FMU can also prioritize the partnership cooperation programs by implementing social forestry programs, instantly absorbing significant community participation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.274

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

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