Diasporic entanglements: Philippine-Toronto entrepreneurs reinventing food, business and belonging pre- and post-COVID-19
Bibliographic record
Abstract
Studies of Philippine migration to Canada primarily chart migrants’ work in health services and caregiving, focusing on the deprofessoinalisation of migrants’ skills and justifiably emphasising issues of precarious citizenship and family separation. This article instead analyses Philippine migrants’ small-scale food businesses in Toronto to demonstrate the mainstream and ‘informal’ strategies entrepreneurs have pursed to establish viable enterprises before and following the COVID-19 pandemic. Drawing on theories of transnational migrant entrepreneurship and people as infrastructure, I demonstrate how entrepreneurs forge socio-economic connections locally and with food businesses in the Philippines to connect society sectors not previously linked or connect them in different ways while flexing to emerging competition from supermarkets offering similar Asian products. By adeptly navigating local-to-international networks and calculative risk taking, I suggest that Philippine-Toronto migrant entrepreneurs fashion an alternative economy in which established business structures can be contested and reconstructed while including relationships rooted in altruism and collective benefit.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".