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Record W2510835071 · doi:10.5539/jms.v6n3p25

Community Use Zone (CUZ) Model and Its Outcome in Malaysia Case Study from Crocker Range Park, Sabah

2016· article· en· W2510835071 on OpenAlexvenueno aff
Peter Voo, Abrar Juhar Mohammed, Makoto Inoue

Bibliographic record

VenueJournal of Management and Sustainability · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceUniversity of Tokyo
KeywordsLivelihoodCorporate governanceBusinessOutcome (game theory)ExternalityLocal communityCommunity participationForest managementOrder (exchange)GeographyForestryAgroforestrySocioeconomicsEcologyEconomicsFinanceEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

<p>The Sabah Parks has been experimenting with a new approach to forest governance, namely Community Use Zone (CUZ), in order to safeguard the forests from ongoing degradation while simultaneously providing opportunities for the affected communities to improve their living conditions and livelihoods. Despite the ongoing discourse to expand this approach, there is limited systematic study to understand the changes in governance as well as linking it to forest and livelihood outcomes. By conducting structured and semi-structured questionnaire interview to CUZ and non-CUZ community members as well as Crocker Range Park staffs, this study clarifies the changes in involvement of local people in rule making and implementation of diverse forest management activities and governance decisions as well as forest and livelihood outcomes from CUZ. The result showed that local people participation in rule making and implementation is enhanced after implementation of CUZ. While the forest outcome remains mixed, the CUZ has brought positive impact to the livelihood of the participants. CUZ also has positive externality to neighboring community in terms of their attitude towards the program.</p>

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)0.003
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 score0.028
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.031
GPT teacher head0.280
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.

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

Citations5
Published2016
Admission routes1
Has abstractyes

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