Food security in Igloolik, Nunavut: an exploratory study
Bibliographic record
Abstract
ABSTRACT This paper reports on an exploratory analysis examining the prevalence of food (in)security in the Inuit community of Igloolik, Nunavut, identifying high risk groups, and characterising conditions facilitating and constraining food security. A stratified cross-sectional food survey was administered to 50 Inuit community members in July 2007. 64% of the participants surveyed experienced some degree of food insecurity in the past year (July 2006–July 2007). Food insecurity among the sample population greatly exceeds the Canadian average. This is cause for concern given the negative physical and mental health impacts that have been documented for low nutritional status. The prevalence and severity of food insecurity differed among participants; females and those obtaining most of their food from the store were at highest risk of food insecurity. Consumption of traditional foods was significantly associated with increased food security. The study supports the need for further research to investigate key trends highlighted by the sample. Preliminary identification of potential trends contributes towards the goal of identifying entry points for policy aimed at strengthening northern Inuit food systems.
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 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 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".