Food & Cultural Center, A New Model for Toronto's Shopping Center - A place for collective memories and un-heard stories of South Asian Immigrants & their future generations in Canada
Bibliographic record
Abstract
With the increase in global migration, the notion of cultural identity is now no longer \nattached to a single place or time and is a continuous process of re-making. This thesis \ninvestigates the role of art, craft, and food in preserving the cultural identity of South Asian \nImmigrants and their future generations in Canada. Building on the existing research on \ndissipating cultural identities of immigrants, this thesis documents the unheard stories of \nSouth Asian Immigrants and food recipes that remind them of home to create a passage of \nembracement. It focuses on the South Asian community settled in Thorncliffe Park, Toronto, \nknown as the ‘Arrival City’. The thesis also envisions ways to collectively re-make their sociocultural \nidentity through the medium of food, craft, and art, and create a place of \nopportunity. The research begins with collecting an archival collection of authentic recipes, \nstories, food traditions, and community initiatives as a medium to be seen and heard through \nstorytelling platforms that will contribute to developing the un-heard narrative of this design-based \nthesis. This design-based thesis will take the steps toward creating a South Asian \ncommunity recreational space in Thorncliffe Park, a source, and a resource to preserve their \nfood traditions, and community-sensitive building language, and hence, create better revenue \ngeneration opportunities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".