Cuisines of Diaspora: Expressions of Iranian Foodways & Culinary Traditions
Bibliographic record
Abstract
The project "Cuisines of Diaspora" was a community-based research inquiry that aimed to expose the status quo of Iranian culinary culture within the decades-strong Iranian diaspora in the North Shore area of Vancouver, BC, Canada. Using a qualitative and interpretive approach with a case study methodology, the research employed multiple tools using semistructured interviews, market observations, photovoice samples, and city archives, as well as other interactions with community members and stakeholders. Nineteen interview responses were obtained alongside a magnitude of data through secondary research findings. The findings suggest a lack of regional Iranian cuisine representation in the North Shore and a thirst for a more diverse offering that reflect the various Iranian provinces. Furthermore, the findings illustrate the growth in dining entertainment options including artistic cultural representations through song, dance, and other art forms. Other findings suggest a strong sense of gastrodiplomacy by the Iranian community, which supports advocacy and promotion of Iranian cuisine. Finally, there is an appetite for more community events that highlight Iranian culture and cuisine and a potential for preserving food culture through community partnerships and sustainable sourcing of food ingredients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".