Gateway to food for thought: cooking up a knowledge diverse library in North Bay
Bibliographic record
Abstract
All Canadians have the responsibility to understand the history of their community, to embolden one another's voices, and celebrate the land as well as the diversity of cultures upon it. This concept is studied through the design of a public food library in North Bay. Research revealed that important cultural knowledge exchange happens around food. Furthermore, literary knowledge transmission can dismiss various forms of cultural information, such as culinary knowledge. This is especially known to be true for Indigenous voices but also for immigrating cultures. Therefore, a public knowledge resource like a library fails to support everyone equally. As such the question to ask is: how can architecture assist in acknowledging independent cultural identity and reveal the knowledge of a place through food? By investigating literature, mapping, food experimentation, and precedents, this research reimagines the definition of library to be more inclusive to a variety of knowledge forms and cultures.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.051 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".