Knowledge and Practice for Implementing Internal Halal Assurance System among Halal Executives
Bibliographic record
Abstract
Food premises that have secured the Halal Certification should comply with the requirements of the InternalHalal Assurance System (IHAS) in their premises. Internal Halal Assurance System (IHAS) is a system thatensures the integrity of halal food at the processing stage, thereby ensuring the production of halal and qualityfood. Although there is an increase in the number of food premises receive the Halal certificate by the MalaysianIslamic bodies, the continuous implementation of the IHAS at the respective food premises is still questionable.To ensure a sustainable practice of complying with the Halal certification system, this study aimed to investigatethe knowledge and skills of the implementing the IHAS among the executives at the respective food premises.This study adopted a qualitative research approach using interview technique on 39 executives at the halal foodpremises throughout the State of Malacca. It was found that the halal executives implemented the IHAS mainlybased on their knowledge in Islam as they lack of knowledge on the requirements of IHAS. Therefore, it issuggested that they should be given continuous training so that a sustainable implementation of IHAS at the foodpremises can be achieved. This can contribute to the good practice of delivering quality and safe food at the foodpremises.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".