Food from here and there, from us and them: characterizing the food system of Rigolet, Nunatsiavut, Canada
Bibliographic record
Abstract
Communities in the Canadian North face many challenges in accessing traditional and market foods. These challenges are attributed to a complex combination of factors including social, economic and environmental shifts, colonial legacies, and the remote geography of communities. Despite these challenges, communities across the North are resilient in maintaining food as a core element of their culture and identity. It is therefore essential to search beyond generalized experiences, to gain a contextual understanding of communities and the intricacies of their local food systems. This thesis adopts such an approach in characterizing the food system of Rigolet, Nunatsiavut. Conducted in partnership with the Rigolet Inuit Community Government, and a community-based research team, the project examines community members' preferences, harvesting, purchasing, sharing, and consumption of both wild and market foods in an effort to answer the research question What is the story of food in Rigolet, Nunatsiavut? Drawing from postcolonial, indigenous, and community based participatory research methodologies, the community-based research team and I adapted two participatory methods for this study. Photo card interviews were conducted with 48 participants, from 27 households in May and June 2013, followed by four phases of month-long food inventories from August 2013 through May 2014, during which 22 households documented all store purchases and wild food harvests. In analyzing these datasets I find that participants' diets are based primarily on store foods, with notable differences in the quantity of wild foods that individuals and households consume, but that the sharing and consumption of wild foods carry significant meaning in terms of identity and culture for all participants. I conclude that Rigolet's food system is a mixed system that combines both market and wild foods, and that the system is resilient given how participants have coped with past and ongoing fluctuations in the availability of species harvested from the land, and the shipment and stock of market foods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".