MétaCan
Menu
Back to cohort
Record W2345756554 · doi:10.29244/jai.2016.4.1.27-42

Perencanaan Pembangunan Ekonomi Wilayah Berbasis Pertanian dalam Rangka Pengurangan Kemiskinan di Kalimantan Barat

2017· article· en· W2345756554 on OpenAlexaff
Nia Permatasari, Dominicus Savio Priyarsono, Amzul Rifin

Bibliographic record

VenueJurnal Agribisnis Indonesia · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAgriculturePovertyInvestment (military)BusinessGovernment (linguistics)Agricultural economicsGovernment budgetDescriptive statisticsEconomic growthEconomicsPublic financeGeographyPolitical science

Abstract

fetched live from OpenAlex

Agriculture-based economic development planning is one of the efforts to reduce poverty in West Kalimantan by synergizing the performance of regional finance and agriculture sectors. The present study aimed at identifying relationship between the performance of regional finance, the performance of agriculture sector and poverty level of West Kalimantan. Analytical tools used to achieve the objectives of this research were descriptive statistics and panel data methods. The results show a positive relationship between the performance of regional finance and agriculture sectors. Gradual reallocation of agricultural budget can be an option for government to determine annual budget. Increase in preparatory investment and reallocation of regional government investment is a necessary policy to give allocation priority for agriculture sectors development. The agriculture sectors, in this case the segment of agriculture sectors on GRDP of West Kalimantan, negatively affect the poverty level. The development of agriculture sectors run by the government should be followed by the increase in human resources quality.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.029
GPT teacher head0.226
Teacher spread0.198 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJurnal Agribisnis IndonesiaSame topicEconomic Growth and Fiscal PoliciesFrench-language works237,207