MIXING RICE-A Support paper for an MFA thesis exhibition
Bibliographic record
Abstract
MIXING RICE is an Artist in Community project was undertaken in the Riversdale Business Improvement District Saskatoon from October 2021 to March 2022 with the curatorial objective to link local Asian and artist communities in Saskatoon through a visual art exhibition. By partnering local artists with Chinese-Canadian restaurant owners based in the Riversdale neighborhood in Saskatoon, this project aimed to introduce Chinese-Canadian restaurant culture to a broader public by immersing both restaurant patrons and interested arts viewers in a physical and visual feast of Chinese culinary excellency. Furthermore, this project aimed to increase visibility for the Asian communities in Saskatoon. The project took the form of a physical exhibition presented in the selected Chinese restaurants in Saskatoon, in addition to an online website that documented the exhibition to disseminate the work more broadly. \nThis support paper examines my research process that led to my exhibition centered onAsian cuisine. I reflect upon the community engagement generated through the exhibition with local artists, restaurant owners, and the audiences. Additionally, the paper is my vehicle to scrutinize how community-based curatorial practice can open dialogue through exhibitions and establish frameworks for future decolonial practices in the curatorial field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.016 |
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".