ECONOMIC DETERMINANTS OF FOOD SECURITY IN NORTHWEST TERRITORIES (NWT), CANADA
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».