Studying in a new home; geographies of international student housing at \nMemorial University of Newfoundland and Labrador
Bibliographic record
Abstract
International students are vital to Canada’s economy and immigration systems, with their contributions surpassing $21 billion annually. Despite their significant economic and immigration impacts, they face housing insecurity, a lack of affordable options, and exclusion from government-funded settlement services. Limited research exists on their experiences outside major metropolitan areas, posing challenges for smaller urban centres like St. John’s and Corner Brook. My research aims to address this gap by examining MUNL international students’ housing experiences and their perceptions of (un)welcoming communities amidst the COVID-19 pandemic. Grounded in concepts of international student mobility, geographies of student housing, and welcoming communities, this study incorporates a mixed methodology, involving interviews, a photovoice study, and a survey. The findings highlighted significant housing challenges faced by MUNL international students, exacerbated by the impacts of COVID-19 pandemic as well as mobility challenges due to the pandemic’s travel restrictions. Results also revealed a generally welcoming atmosphere in NL communities, with student participants describing friendly interactions and a sense of neighbourhood but expressed their incomplete sense of being at-home. Thus, this thesis calls for comprehensive solutions to address the housing needs of international students in NL while contributing to broader discussions on immigration and housing.
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 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".