HALAL IN THE FOOD INDUSTRY AROUND THE GLOBE
Bibliographic record
Abstract
The concept of Halal is well understood and practiced Muslims. Muslims are restricted to only consuming foods that are certified as Halal. However, today, the consumption of Halal food is no longer regarded only as a religious obligation for Muslims, but is also sought after by non-Muslim society due to the rising health concern as Halal foods are often classified as ones that have high quality from the perspectives of safety and hygiene. The fact that there are already 1.9 billion Muslims in the globe is indisputable proof that the halal food sector is promising for both Muslim and non-Muslim participants in the industry. Many Muslim-minority countries, such as New Zealand, Canada, the United Kingdom (UK), Australia, the United States of America (USA), India, and Argentina are also exporting Halal foods to foreign countries as they believe that this can generate substantial revenue for them. Nevertheless, low awareness of the concept of halal, uncertainties regarding the ingredients used in the products, and misleading information on a product’s packaging are a few of the challenges in the Halal food industry. In order to popularize the concept of Halal to more non-Muslims, the authority, plays a significant role in this scenario by providing public information related to the concept of Halal as well as taking more stern actions in combating the occurrence of Halal food frauds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".