Empirical Analysis of Mcdonald's Fast-Food in Malaysia Based on Halāl Food Regulations in Sūrah Al-Māʾidah
Bibliographic record
Abstract
This study tends to monitor the phases of fast food chains industry at MacDonald’s in Kuala Lumpur at Malaysia and check its compliance with Sharia restrictions based on ḥalāl food regulations in Sūrah al-Māʾidah. Besides, this study eyes to make sure that the food is edible and safe from any kind of ingredient or component that might harm the consumer’s health or have any negative impact on his religion. The deductive method has been used to conclude the most important sharia restrictions related to the acquisition of ḥalāl food. In addition, a qualitative research method with a case study was used to monitor the operations of the fast food industry in McDonald's in Malaysia. Furthermore, a set of questions has been used for an interview with two senior staff members of McDonald's in order to gain a deep understanding of industry processes ḥalāl food. These questions were developed based on ḥalāl food regulations in Sūrah al-Māʾidah. In this study, eight emergent themes have been discovered while doing analysis which are: Food Hygiene and Safety, Ingredient, Equipment and Environment, Packaging, Processing, Storage and Transportation, Staff and Sharia Advisor. The result of this study proved that Mcdonald's Fast-Food in Kuala Lumpur comply with the halāl food regulations stated in Sūrah al-Māʾidah. This study might benefit the international and domestic food companies with a greater concern on Sharia requirements on food handling from production to marketing and from preparation to serving.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".