Bibliographic record
Abstract
In this paper we analyze two March 2010 events in Ottawa, Canada involving the preparation and consumption of seal-meat: one an Inuit seal feast, held at a Inuit community center, in which raw seal was carved and eaten in accordance with traditional Inuit practices; the other a "seal lunch", held in the Parliamentary Dining Room for Members of Parliament, in support of the Canadian seal-hunt. Methodologically, we make use of both participatory action research and detailed textual analysis of media reports, and frame our analysis in terms of moral geographies, social and cultural values associated with food, and meaning-making systems embedded in discourses, which serve to construct and constitute particular power relations. Doing so leads us to claim that the two seal-meal events drew on and conveyed radically different meanings. The Inuit meal, though not overtly political, represented an act of food sovereignty and a claim to Inuit territoriality in the city. The Parliamentary seal lunch, by contrast, had a clear political aim, as a form of protest against the European Union decision to ban seal meat and other products. Yet, while purporting to support Inuit seal-hunting, the Parliamentary meal effectively communicated the utter foreignness of seal meat and Inuit foodways with respect to Western tastes and discourses about food and environmentalism—a fact that emerges through our ethnographic and media analysis of the two seal lunch events.
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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 | 0.002 | 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 | 0.010 | 0.032 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".