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Record W2149627499 · doi:10.5539/ass.v11n4p1

Financial Problems among Farmers in Malaysia: Islamic Agricultural Finance as a Possible Solution

2015· article· en· W2149627499 on OpenAlexvenueno aff
Muhammad Hakimi Mohd Shafiai, Mohammed Rizki Moi

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyAgricultureGeneral partnershipIslamBusinessFinanceGovernment (linguistics)LoanProduct (mathematics)EconomicsMarket economy

Abstract

fetched live from OpenAlex

This paper attempts to look at the possibility of collaboration of Islamic agricultural finance in agricultural land development realizing the farmers’ financial problems during the case study and the government efforts in invigorating agricultural sector in Malaysia. Agricultural product and Loss Sharing (aPLS) which based on the contracts of al-muzara’a and al-musaqa from Islamic jurisprudence, partnership between the landowner and the farmer in brief, was highlighted in this paper. Agro Bank has the potential to employ Islamic agricultural finance realizing its roles in developing agricultural sector in Malaysia and its direction to be a full-fledged Islamic bank. The case study was carried out based on two methods that are interviews and questionnaire. These two methods have been used among the farmers under the one-off subsidy program by the Department of Agriculture in the selected six states in Malaysia. It was found that the farmers at the survey areas faced the financial problems during the second cycle cultivating yet the farmers have the capability in saving at the financial institutions. Thus, the main theme of this paper is that how Islamic agricultural finance can play a positive role and possible solutions to the farmers in Malaysia and eventually promoting agricultural development in Malaysia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.223
Teacher spread0.211 · 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

Citations34
Published2015
Admission routes1
Has abstractyes

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