Institutionalizing Distinctiveness: Halal Food Business Evolution and Muslim Integration in Canada
Bibliographic record
Abstract
The halal food industry is growing exponentially all over the world and it is therefore receiving increasing interest from researchers. In a migratory context, it offers diversification to the host societies, including changing the food industry. Halal food businesses and products are becoming very common within non-Muslim societies, and they are also gaining an important share of markets in the mainstream food industry. To better understand how the halal food business has developed, and what are its particularities and impacts on Muslim communities and their host societies, we studied halal food businesses in Canada. We conducted more than 30 interviews with immigrant entrepreneurs from different countries of origin who started and manage halal food businesses. By employing optimal distinctiveness theory (Brewer, 1991; Zhao et al., 2017), we discovered that the halal food industry in Canada has followed a non-conventional growth process. By examining our data, we developed a model that explains how the halal-food business model evolved over time and how it impacted the integration process of newcomers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".