Moosemeat & Marmalade: Analyzing Mediatized Indigenous Food Cultures on TV
Bibliographic record
Abstract
In recent years, we have seen an increase in media discourses about food, especially in the form of television food shows, in- cluding instructional cooking shows, travel shows on culinary destinations and cooking competitions. However, these shows mostly bring culinary experiences to global attention framed so as to satisfy ‘normative appetites’ and teach pragmatic culinary knowl- edge that promotes regimes of culinary taste (De Solier 2005). Indeed, while Canadian TV screens do reflect the multicultural essence of the nation, with cooking programs focusing on Italian, French, Japanese, and other ‘fancy’ culinary cultures, no space is de- voted to Indigenous eating traditions on mainstream TV networks. Studies have pointed out that Indigenous cooking knowledge, habits and ingredients have long been neglected and Indigenous food sovereignty violently negated as an effect of cultural and lin- guistic imperialism. However, since the 1990s, Indigenous cultural knowledge has undergone a process of mediatization and has been profoundly affected by the rise of mediating technologies (LaDuke 1999). The Aboriginal Peoples Television Network (APTN), for instance, broadcasts cultural contents, re-mediated for television consumption, produced by and for Indigenous peoples, including Indigenous food knowledge. The present study aims to investigate how food is deployed counter-discursively in the television space of the Aboriginal Peoples Television Network (APTN), a Canadian cable TV channel made for and by First Nations, Inuit and Métis people, in light of the broader process of Indigenous cultural revitalization and Indigenous food sovereignty.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".