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Record W4378836490 · doi:10.18280/ijsdp.180511

The Impact of Small-Scale Oil Palm Plantation Development on the Economy Multiplier Effect and Rural Communities Welfare

2023· article· en· W4378836490 on OpenAlexvenueno aff
Almasdi Syahza, Dahlan Tampubolon, Mitri Irianti, Geovani Meiwanda, Brilliant Asmit

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
FundersDirektorat Jenderal Pendidikan TinggiKementerian Pendidikan, Kebudayaan, Riset, dan TeknologiUniversitas Riau
KeywordsWelfarePalm oilAgricultural economicsNatural resource economicsRural economyMultiplier (economics)Rural developmentEconomicsScale (ratio)Rural areaEnvironmental scienceBusinessAgroforestryGeographyAgricultureMarket economyPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

This research aims to analyze the multiplier effect of small-scale oil palm plantations and the welfare of the community in rural areas during the first cycle.This developmental research conducted in Indonesia's largest oil plantation area, the Riau Province.Data were collected from farmers using the rapid rural appraisal technique through participatory approach.The data was analyzed to determine the regional economic multiplier number and the social welfare growth index.The results showed the magnitude of the multiplier effect index impacts the welfare index of rural communities.Oil palm farmers in rural areas have a more stable economy, and their consumption style follows the urban communities in creating an attitude of pride.The average family income of small-scale oil palm was more than five times bigger than non-oil palm farmers.In an effort to accelerate the economy in rural areas, especially in oil palm-producing areas in Indonesia, government policies related to pricing at the farm level are urgently needed to have an impact on increasing the income of small-scale farmers in rural areas.

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)0.002
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 score0.118
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.255
Teacher spread0.242 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Sustainable Development and PlanningSame topicOil Palm Production and SustainabilityFrench-language works237,207