Ethnic Foods and Immigrant Entrepreneurship: The Production and Marketing of Korean Foods in Toronto, Canada
Bibliographic record
Abstract
This study uses an oral life history perspective to examine Mr. Jasun (Jason) Koo’s entrepreneurial activities in Canada’s ethnic food manufacturing industry. He is a Korean immigrant entrepreneur who co-founded and runs the Toronto-based PH Food Company, which has produced tofu, dumplings, rice cakes, and noodles since 1976. Previous studies demonstrated that Korean restaurants and grocery businesses have been closely associated with forming and developing Korean communities in North America. However, studies on local production and marketing of Korean food items in Canadian society are rare. The main research findings are as follows: Mr. Koo has utilized various ethnic and transnational resources to start and operate a Korean food manufacturing business. Furthermore, he has grown and diversified the company’s customer base, from Korean to multiethnic consumers over the years, thanks to awareness of tofu as a health food product, commonality among Asian food culture, growing popularity of Korean culture and food among Canadian consumers, and his son joining the business. This study is expected to contribute to elucidating the history of Korean immigration to Canada, Korean entrepreneurs’ involvement in Canada’s food industry, and the spread and significance of Korean food culture overseas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".