ECONOMIC DETERMINANTS OF FOOD SECURITY IN NORTHWEST TERRITORIES (NWT), CANADA
Bibliographic record
Abstract
ABSTRACT\nIn recent years, food insecurity, specifically food access and food availability has deteriorated in many parts of the world, including the Northwest Territories (NWT) of Canada. Food insecurity is associated with adverse mental health, obesity, chronic illnesses, and poor academic out-comes. Recent research in NWT aimed at understanding the determinants of food insecurity suggests that high transportation costs, food spoilage, inadequate distribution, and the lack of sales alternatives result in severe food insecurity. These studies, however, are based on small sample sizes from selected communities.\nThis thesis studies the current trends of food insecurity and its correlation with socioeconomic factors across NWT’s six regions and 34 communities. To do so, I gathered secondary data at community level from various sources, including the NWT Bureau of Statistics and Statistics Canada over different time periods. The measurement of food insecurity rests on the indication of what percentage of households per community were worried about not having enough money to buy food in 2018. This indicator reflects food insecurity in the sense of a lack of financial resources to access food and relates to the demand side. The socioeconomic factors considered relate to both the demand side and the supply side as they can affect the percentage of house-holds worried of not having enough money to buy food through factors that affect households’ ability to access food and factors that affect food availability, respectively. \nThe results indicate a north-south divide: In northern regions such as Beaufort Delta, Sahtu, Thcho, and Dehcho, 31%, 31.2%, 55.1%, and 31.5% of households, respectively, are concerned about not having enough money to buy food, while in southern regions such as South Slave and Yellowknife, the percentages are 18% and 17%, respectively. On average, the four northern regions are more than twice as likely to be food insecure as the two southern regions. Also, the results of the descriptive analysis show that regions with more dispersed households, no active mines, and only a few small-sized grocery stores are associated with higher levels of food insecurity. Furthermore, the Ordinary Least Squares (OLS) results show that communities with higher population densities, and a higher percentage of its population participating in tradition-al activities are associated with higher food insecurity. Also, communities that benefit from Nutrition North Canada’s (NNC) food subsidy, as well as communities that have a more educated population, or better transportation facilities such as all-weather roads and airport facilities are associated with lower food insecurity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".