Serving Resistance on the Menu: The Cultural Politics of Filipino Cuisine in Winnipeg and Ottawa
Bibliographic record
Abstract
This thesis explores the cultural politics of Filipino cuisine in Canada.Filipinos are the fourth largest visible minority group in Canada yet their cuisine remains underrepresented in the Canadian foodscape compared to other Asian groups.By comparing Winnipeg and Ottawa's contexts, I explore how Filipino cuisine entrepreneurs "do" Filipino cuisine through their establishments.I also examine potential explanations as to why Filipino cuisine is not mainstream.The findings suggest that the underrepresentation of Filipino cuisine can be attributed to structural barriers (colonialism and institutional racism) and the low incidence of Filipino entrepreneurship.Through culinary entrepreneurial practices, Filipino cuisine entrepreneurs engage in a politics of resistance and identity work.For some, the production of Filipino cuisine is implicated in the struggle against cultural assimilation.For others, it is an act of cultural pride and a politics of representation that seeks to disrupt the "hypervisibility" and "invisibility" of Filipino-Canadians and Filipino cuisine.chance.Through his approachable teaching style, I fell in love with sociology, so much so that I decided to take his class as a student the following year.As a teacher, he goes above and beyond for his students and if it were not for his help, I would have not mustered the courage and selfconfidence to apply to graduate school.To Curt, as always, I thank you for being such a beacon of support even until now.I also thank you for being such a great role model.I can only hope one day I can inspire my students like you have for me and for countless others.I would like to thank my peers and classmates for their lending ears and for helping me brainstorm ideas on my thesis.Your feedback, critiques, and second opinions were all very helpful and kept me in check.I would especially like to thank Marie Coligado for being so patient with me and providing critical insights and feedback during the thesis writing process.Marie was especially helpful with our discussions regarding the differences and similarities between the Filipino communities in Winnipeg and Ottawa.A very large thanks to my friends in Winnipeg and in other parts of the world.Since undergrad, you all encouraged me to pursue sociological studies on critical race and ethnicity studies and food.Throughout the years, my friends would send me articles relating to my research interests through e-mail or social media.I like to extend my gratitude to Joey Felizardo who accompanied me during my participant observations to Filipino restaurants and introduced me to other Filipino restaurants in the city.I would also like to thank Palak Dhiman for her support and attending my thesis defence.Your presence definitely served as a calming effect.Thank you to all my friends who listened to me as I rambled on about my dissertation.Thanks for believing in me and snapping me out of my moments of self-doubt and impostor syndrome.To my mom, dad, and my brotherthank you for being such a great family unit and for always supporting my studies.I am grateful to my mom and dad for their sacrifices so they could provide for me and Matthew.My mom and dad parented me in a way that fostered my independence and critical thinking, even if it was not easy, and even if their parenting styles were criticized by family members and others.I thank my mom and dad for never giving up on me and for letting me pursue my dreams freely.Mom and dad, your influence has shaped me as a sociologist today.Mom, thank you for
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".