Factors Influencing the Low Demand for Salam Financing Contracts in Indonesia – Concept Paper
Bibliographic record
Abstract
Salam financing is significant in the agricultural sector, especially for seasonal food crops. It has many benefits for the community, particularly for poor farmers, and it is appropriate in the agriculture sector. This paper aims to discuss the factors influencing the low demand for Salam financing contracts that are impactful to either buyer, the bank, or the seller, the farmer. This study adopts a qualitative approach, which includes reviews and analysis of secondary data in relevant literature and documents relating to Salam financing contracts in the Indonesian banking industry. The significant findings of this study are the identification of human resources in Islamic banks that should be improved in terms of their knowledge of Islamic banking products. It is crucial also to know how to innovate for a sustainable and implementable product. In order to address the challenges in Salam financing, it is proposed to reduce internal and external issues with Salam financing. This study adds new knowledge to the existing literature by optimizing the Salam contract in Islamic banking to overcome capital problems in the farming sector and collaborating with agricultural insurance to cover capital costs that farmers cannot pay when crop failure occurs. This study will recommend the methods to overcome the problems stated faced by Islamic banking, including using fintech to counter the issues.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".