RASOI: interior design approach to cultural revitalization through food sovereignty
Bibliographic record
Abstract
This document examines food sovereignty though an interior design lens in the province of Manitoba, Canada, by way of RASOI, a community food center located at 814 Main Street in Point Douglas, Winnipeg, Manitoba. The design framework for the community center is based on contemporary design theories and concepts that address the issue of food sovereignty. These issues is informed by writers and theorists including Nettie Wiebe, Kevin Wipf, Pat Vanderkooy, Valerie Tarasuk, Carolyn Dimitri, Stephen Penner, and Dawn Morrison. Additionally, patterns of production and consumption of foodways are explored to further understanding of Point Douglas journey towards food sovereignty. To guide the design of the community center, the focus is on Canadian Indigenous, contemporary Western, and Kashmiri foodways. Each contributes something different: traditional Indigenous culinary values informed by Jaime Cidro and Tabitha Martens; new urban farming technologies in the case of Western foodways from Sarvenaz Pakravan; and insertion of the author’s Kashmiri culture for comparison. RAOSI focuses on providing equitable opportunities to Point Douglas through production spaces centered around indoor urban agriculture. The design of spaces for food distribution furthers food sovereignty and community connection through activities that gather, teach, and strengthen traditional Canadian Indigenous food knowledge. The convergence of traditional Canadian Indigenous, Western, and Kashmiri customs is fostered through the theories and concepts of third space by Homi K Bhabha, transculturation by, by Mary Louise Pratt, Two-eyed Seeing by Elder Albert Marshall, and lastly Nicole Bell and Robin Wall Kimmerer’s analysis of Bimaadiziwin. Additionally, sustainable strategies from Janine Benyus’s description of biomimicry and Graeme Brooker and Sally Stone’s discussion of adaptive reuse influence the sustainable design strategies within the building to blend Indigenous values and Western technologies. These theories and concepts are implemented through spatial design implications with architectural elements including form, hydroponic farming systems, open circulation, natural lighting, local material specification, and narrative.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".