Jurisprudence and demand for halal meat in OIC
Bibliographic record
Abstract
Purpose The purpose of this paper is to estimate the import demand function for halal meat in member countries of the Organization of Islamic Cooperation (OIC) and to suggest some policy recommendations for OIC members that can enhance intra-OIC halal meat trade. Design/methodology/approach By using an augmented gravity model, this study empirically estimates the major determinants of halal meat import demand in OIC member countries. Moreover, a major determinant is the difference in Islamic jurisprudence (fiqh). Findings The results of this study show that the variation in Islamic jurisprudence is one of the primary determinants of intra-regional trade of halal meat import demand in OIC member countries. Research limitations/implications Although trade flows are set up in several years and lag variables are well capable to examine trade flows, this study only includes the static nature of halal meat trade flows toward selected top 20 OIC member countries. Practical implications This study suggests that developing a common halal meat market and one halal certification body under the OIC can enhance intra-OIC halal meat trade, this may be a challenge given the five diverse interpretations of halal meat within Islamic jurisprudence among OIC member countries. Originality/value This paper identifies the role of Islamic jurisprudence (fiqh) in determining the import demand of halal meat in OIC countries, which has not been addressed in empirical literature. It also provides some policy implications to ameliorate the declining trend of intra-OIC trade flows of halal meat.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".