Social Networks as a Coping Strategy for Food Insecurity and Hunger for Young Aboriginal and Canadian Children
Bibliographic record
Abstract
Traditional foods and food sharing are important components of Aboriginal culture, helping to create, maintain, and reinforce social bonds. However, limitations in food access and availability may have contributed to food insecurity among Aboriginal people. The present article takes a closer examination of coping strategies among food insecure households in urban and rural settings in Canada. This includes a comparative analysis of the role of social networks, institutional resources, and diet modifications as strategies to compensate for parent-reported child hunger using national sources of data including the Aboriginal Children’s Survey and the National Longitudinal Survey of Children and Youth. Descriptive statistical analyses revealed that a majority of food insecure urban and rural Inuit, Métis, and off-reserve First Nations children and rural Canadian children coped with hunger through social support, while a majority of urban food insecure Canadian children coped with hunger through a reduction in food consumption. Seeking institutional assistance was not a common means of dealing with child hunger, though there were significant urban-rural differences. Food sharing practices, in particular, may be a sustainable reported mechanism for coping with hunger as such practices tend to be rooted in cultural and social customs among Aboriginal and rural populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".