Bibliographic record
Abstract
The increasing demand for halal products, including goods and services, every year, especially for food and beverages, has resulted in a growing need for products with halal guarantees. Along with the increasing trend of the global demand, it has resulted in an increase in producers of halal food and beverages in both Muslim and non-Muslim countries. In addition the demand for halal tourism is also increasing. Indonesia is one of the largest Muslim countries in the world. However, there are still many Muslim consumer actors and Muslim producer actors who do not yet have an awareness of the importance of complying with the provisions of Islamic law in consuming and producing goods and services. There are still many restaurants and hotels that serve food and drinks that are not certified halal. There are still many food, medicinal and cosmetic products that are not halal certified. But now many secular countries such as France, Canada, Australia, the United States, Britain are also halal certified with the aim of meeting the Muslim demand for halal products for food and beverage, including for halal tourism. Starting from the development of the halal industry both in the fields of food, beverages and services, an International Seminar was held, which provides a more complete understanding of halal products, current halal developments and can serve as motivation to produce halal products, providing research results from the topic of halal development. The international seminar, entitled International Conference on Halal Development, listed speakers from several countries able to provide an overview of the halal development of several countries. This book contains a selection of papers from the conference.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.940 | 0.932 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".