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Record W4405832786 · doi:10.5267/j.uscm.2024.12.003

Clove commodity issues in productivity improvement and sustainability based on policy elements in Indonesia

2024· article· en· W4405832786 on OpenAlexvenueno aff
Achmad Tarmizi, Sahlan Sahlan, Henky Henanto, Andjar Prasetyo, Yanter Hutapea, Mohammad Sofyan Budiarto, Hasim Ashari, Mu’man Nuryana, Husein Avicenna Akil, Hadi Supratikta, Sitti Ramlah, Baharudin Baharudin, Asmin Asmin, Arnis Rachmadhani

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsProductivitySustainabilityCommodityBusinessAgricultural economicsNatural resource economicsEconomicsEnvironmental economicsEconomic growthFinance

Abstract

fetched live from OpenAlex

The role of the clove industry in Indonesia's economy is crucial, as it makes a substantial contribution to the GDP and export figures. Despite experiencing growth overall, clove farmers face various challenges such as price fluctuations, climate change impacts, and limited market access. By analyzing data spanning from 2014 to 2021, this research anticipates a continuous increase in production levels but foresees a decrease in export numbers. Proposed policy measures include providing financial assistance, promoting education, enhancing infrastructure, developing markets, and ensuring environmental sustainability. It is crucial to focus on credit availability, incentives, and subsidies, while also improving agricultural education, infrastructure, and sustainable farming methods. A comprehensive approach across eight key policy areas is necessary to strengthen the clove industry in Indonesia. By addressing these challenges holistically, the clove sector can look forward to a resilient and sustainable future, maintaining its importance in the country's economic landscape.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.266
Teacher spread0.260 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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