A history of food and nutrition in Indigenous communities in Canada, 1962-1985
Bibliographic record
Abstract
This thesis explores the history of food and nutrition in Indigenous communities in mid- to late-twentieth century Canada. It does so through tracing changes in state policies and procedures, as well as Indigenous approaches to foodways, health, and healing from the 1960s into the 1980s. It looks at how shifting discourses of nutrition and food science impacted educational materials and programs for Indigenous peoples, often aimed at mothers and children in Indigenous communities, and highlights a moment of possibility in the early 1980s when Indigenous peoples were increasingly being consulted on and leading state programs tailored to their communities and cultures. It aims to privilege the experiences of Indigenous peoples through the choice of sources and attention to Indigenous methodologies. It therefore includes discussion of Indigenous activism and its impact on health and healing, food production and preparation, land use and agriculture, and data collection used for food and nutrition surveys. Central to this dissertation is the concern that Indigenous peoples have been studied, pathologized, and racialized as part of a long history of settler-colonialism in Canada. Many of the state nutrition projects that informed policies and programs during the period were organized and carried out by non-Indigenous experts and their cadres, with a very top-down approach. These mirror colonial initiatives dating back a century in the most heavily studied regions, singling out Indigenous peoples as a unique category of subjects to be studied separately from the rest of the population. Accordingly, it focuses heavily on Manitoba and the Northwest Territories, regions that were often the focus of Medical Services Branch initiatives. In studying the history of ‘Aboriginal nutrition’ as a growing field of expertise during the period, this dissertation challenges narratives of decline, dispossession, and displacement of Indigenous peoples that have dominated Canadian history. It contributes to the growing literature that works instead to centre Indigenous peoples and their experiences of modernity, in this case their foodways and nutritional knowledge. It does so by exploring gender, food, and emotions as categories of historical analysis helpful in peeling back the layered relationships between Indigenous peoples and representatives of the state.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".