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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".