Exploring First Nation Elder Women’s Relationships with Food from Social, Ecological, and Historical Perspectives
Bibliographic record
Abstract
BACKGROUND: The ongoing negative health effects of colonization have disproportionately affected Indigenous women, who are disproportionately affected by diabetes, food insecurity, and undernutrition. Indigenous women also perceive their health less positively than men do. This article draws theoretically from the socio-ecological model to explore health inequalities experienced by Indigenous women associated with the intergenerational effects of the residential school legacy, specifically related to food practices. OBJECTIVES: Study objectives were to describe and compare the historical context of present-day urban and rural food environments, and explore the hypothesis that food insecurity may be associated with cultural loss resulting from the intergenerational trauma of residential schools in this region of southwestern Ontario, Canada. METHODS: Framed by a larger community-based participatory study, life history interviews took place with 18 Elder women living on- and off-reserve in southwestern Ontario, Canada. RESULTS: Women discussed painful circumstances of displacement from the land and social disconnection from families and communities. The 10 participants who were residential school survivors conveyed the intergenerational effects of loss, responsibility, lack of support, and an altered sense of identity as narratives of survival. Six women had moved away from their home communities, which created challenges to fully engage in local food procurement and sharing practices. These altered geographies present practical limitations, along with apparent mechanisms of social and cultural exclusion. CONCLUSIONS: Research on Indigenous Peoples' food systems requires further analysis of the root causes of disparities in the context of societal and gender relations. Food sovereignty has been the domain of women, who have led movements aimed at both social and environmental justice. Unraveling the historical, social, and environmental determinants of Indigenous food knowledge will support and guide community and policy recommendations, highlighting the ongoing effects of residential schooling and other indirect examples of environmental dispossession that have disproportionately affected Indigenous women.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".