ᐅᑎᕈᒪᔪᖓ \nUtirumajunga \n (I Want to Return): \nA Look at Situations of Homelessness Among Inuit Women in Montreal
Bibliographic record
Abstract
Homelessness among Inuit in urban settings is a topic that has, until recently, largely been ignored. Academic research strictly focused on Inuit women who leave their northern home communities due to a lack of critical resources and move to southern cities despite not having permanent housing there has yet to be written. The purpose of this research is twofold. First, it seeks to answer the question of how Inuit women navigate situations of homelessness in Montreal, Quebec. Further, it explores whether their decision to relocate to Montreal is directly related to the challenges Inuit women experience in Inuit regions. This thesis draws on three interviews with Inuit women living without permanent housing in Montreal. Two overarching categories with five respective sub-themes were identified through a thematic analysis of the qualitative data. These themes account for the similarities regarding the women’s experiences with homelessness in Montreal and their living conditions within Inuit Nunangat. Finally, the transition back and forth from their northern communities to Montreal, and the conditions that prompt this phenomenon, are explored. Taken together, these results provide an account of homelessness among Inuit women in Montreal and the northern circumstances that led them to their current situation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".