Wild Cuisine and Canadianness: Creeping Rootstalks and Subterranean Struggle
Bibliographic record
Abstract
Canada is commonly depicted as a rugged wilderness. Defining the characteristics of its food as wild is a contributing factor in this narrative. While there may be some truth to this image, there are also overlooked implications in perpetuating links between the notion of Canada as a nation, and the trope of wilderness as its defining feature. In this article, I draw on visual analysis as well as theory from sensory studies to complicate the concept of “wild” food at the root of discourse on Canadian cuisine. The focus of this analysis is a case study of wild berries on the northeastern coast of Québec, Canada. Throughout the article I quote from interviews that I conducted with Anglophone, Francophone, and Innu locals of Québec's Lower North Shore. The intimate experiences of residents with the foods that grow in their home do not connect smoothly with representations of wilderness in promotional materials for wild berry products and tourism in the region. In fact, personal accounts of picking, preparing, and eating wild berries complicate master narratives of wild Canadian cuisine, thus enriching this country's national food culture through complexity. These stories show that wilderness is not a state of purity but a fiction that obscures the multifaceted natural-cultural negotiations among humans, plants, animals, climate, and more in the making of what we call “wild.”
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".