Bibliographic record
Abstract
Islamophobia continues to be on the rise in Europe and other regions, such as Australia, Canada and the United States. It is now a global phenomenon which has multiple manifestations and is generated at different layers of society, and does not only affect citizens1 in non-Islamic countries, but has recently shown a new expression: the target has shifted towards Islamic economies, and more specifically towards the halal trade. Emerging economies in the region of Asia-Pacific and the Gulf are net importers of halal products (particularly foodstuff), which, paradoxically, are produced in non-Islamic economies. A report commissioned by the Dubai government, and researched and written by Thompson Reuters and Dinar Standard,2 valued the halal food and beverage (F&B) market at US$ 1.37 trillion in 2014. That represented 18.2% of the total global F&B market. In addition, the youthful population of the Muslim world - with 60% under the age of 30 - indicates that demand for halal products and services is likely to continue its upward growth curve and become an increasingly influential market over the next decade. This tremendously attractive market niche, combined with the slow growth of the economies of Europe, Canada, Australia and the United States, has prompted many industries to seek halal certification and to adapt their products and services to the requirements of Muslim consumers worldwide, including the significant minorities already living outside Islamic economies.3 However, some newcomers to the halal global market have found that there is another obstacle to overcome, apart from those already present in global trade: Islamophobia. In this article, I will explore the many expressions of Islamophobia aimed at stopping the growth of the halal market, and the different policies and attitudes of governments and institutions when confronted with the need to balance economic growth with cultural misunderstandings and hatred. I found systematic attempts to undermine the halal food industry made by some European Members of Parliament, claims of animal cruelty sparked by animal rights groups, bans on halal sacrifice in the meat industry, the “boycott-halal” on-line campaign, alleged funding of terrorism, threats and other expressions of hatred that have managed to prevent many businesses from accessing the emerging halal market.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".