Bibliographic record
Abstract
In Canada, there are vast differences between the state of accommodation/housing, health, social inequalities, education and economic conditions for people in the northern and southern regions of the country. Overcrowding in Inuit Nunangat is a direct result of the promises made by past government policy that led to Inuit people settling in sedentary communities in the North on the understanding that they would be provided with social welfare. However, these welfare programmes proved to be either insufficient or non-existent for Inuit people. Therefore, Inuit are living in overcrowded homes in Canada, resulting in a severe housing shortage, poor-quality housing and homelessness. This has led to the spread of contagious diseases, mould, mental-health issues, gaps in education for children, sexual and physical violence, food insecurity and adverse challenges for the youth of Inuit Nunangat. This paper proposes several actions to ease the crisis. First, funding should be stable and predictable. Next, there should be ample construction of transitional homes which could be used to accommodate people before moving them into proper public housing. Policies regarding staff housing should be amended, and if possible, these vacant staff houses could provide shelter to eligible Inuit people, which could help lessen the housing crisis. The advent of COVID-19 has made the issue of affordable and safe housing more serious because without safe housing, the health, education and well-being of the Inuit people in Inuit Nunangat are in peril. This study focuses on how the governments of Canada and Nunavut are dealing with this issue.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".