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Record W4401130193 · doi:10.18280/ijsdp.190736

Governance Policy Sustainable Peatlands Through Community Economic Development in Bengkalis Regency

2024· article· en· W4401130193 on OpenAlexvenueno aff
Zulkarnaini Zulkarnaini, Febri Yuliani, Anuar Rasyid, Dadang Mashur

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPeatCorporate governanceSustainable developmentBusinessEnvironmental planningEnvironmental resource managementNatural resource economicsGeographyEnvironmental sciencePolitical scienceEconomicsArchaeology

Abstract

fetched live from OpenAlex

Peatlands face considerable vulnerability to environmental conditions, posing a significant challenge to the economic well-being of local communities residing in these areas.Striking a balance between promoting the local economy and preserving the hydrological integrity of peatlands is crucial.This paper delves into the enhancement of a business model established through the Participatory Action Research (RAP) method, implemented in a 3 hectare pilot site currently undergoing monitoring and assessment.The findings from this monitoring and assessment phase will inform the second stage (loop) of the RAP process, facilitating refinements to the tested business model.Drawing insights from the Tanjung Leban Village in Bengkalis Regency, Riau Province, as a case study, it is deduced that a peatland-based commodity business model should incorporate pre-production activities.This precautionary measure aims to prevent peat subsidence and preserve the hydrological functionality of the area.Additionally, a temporal segregation of key activities (pre-production, production, and post-production) enhances the granularity of identifying required activities and resources.The study underscores the necessity of designing programs and interventions, such as peatland restoration initiatives, family welfare programs, and business incubator programs, with a focus on collective benefits for groups rather than individuals.To ensure an equitable distribution of benefits and costs among participants, program designers and implementers must carefully consider these dynamics.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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