Decolonizing the learning of sitopias in Toronto
Bibliographic record
Abstract
The Canadian Cuisine Photography Challenge is a pilot experiential learning activity created at Ryerson University for the class FNU100-Canadian Cuisine: Historical Roots, a first/second year liberal studies course offered to students from diverse programs and cultural backgrounds. This activity is both a fun challenge and a required course assignment. It aims to engage students with Canadian cuisine and is inspired by a decolonial pedagogical approach (Mignolo & Walsh, 2018; Santos, 2018) to food studies, and elements of photovoice methodology (Wang & Burris, 1997). The Canadian Cuisine Photography Challenge consists of a field trip to different food places or sitopias in Toronto with the goal of learning about their histories and developing an appreciation of the role of food and people in the city (Newman, 2017). The activity includes a map, instructions and a set of ten challenge questions that students answer through photographs taken during their field trip. The field trip is followed by students’ presentations in class and a reflection of their experiences. In the first phase of the project, students explored two sitopias: Kensington Market and Chinatown. This paper will first describe the co-creation of the Canadian Cuisine Photography Challenge with students from the School of Nutrition at Ryerson University. This was a collaboration between the course instructor, two School of Nutrition students and included input from other students who had previously taken the course. It will present key learnings from the feedback of students who participated in the challenge in the fall of 2019, including how they described their experience, what they learned and suggestions for the future developments of this project. In particular this field reportwill discuss the use of a decolonial pedagogy in food studies, recognizing and challenging a Western hegemonic view of food places as representative of Canadian cuisine, while at the same time outlining the co-construction of experiential learning activities to engage students and provide content that reflects the multiple identities and food cultures of Canadians in Toronto. The main purpose of this field report is to share our experience co-creating and implementing this pilot project as one contribution towards decolonial food pedagogies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".