Neo-liberalization, Devolution, and Refugee Well-Being: A Case Study in Winnipeg, Manitoba
Bibliographic record
Abstract
Canadian housing policy and the categorical distinction between refugees, who receive government support for housing, and those who do not, demonstrate that obligations to refugees are increasingly being met by ethno-cultural communities, religious groups, refugee kinship networks, and community-based organizations. Devolving authority and responsibility for the provision of housing and of settlement services to the level of community does provide opportunities for input and decision-making autonomy on the part of community-based organizations (CBOs). 'Community' undoubtedly has a robust function in refugee service provision; however, such a function is realized amidst structures of differential market access and market power, as well as varying degrees of familiarity and capability within the local environment. Using a case study situated in Winnipeg, Manitoba, this article considers how refugee status and community actors contribute to refugee housing outcomes in a context in which refugee well-being is increasingly becoming 'neo-liberalized', or made a 'private' affair predicated on market processes and voluntary contributions. Community grounded research can help academics guard against categorical assumptions about community and institutional change, analysis which tempts us to abstract from the particular and ascribe such change to the often ungrounded, but always powerful, meta-value and meta-narrative of neoliberalism. Such research can write agential actors back into the narratives and analysis of wide-scale political, economic and social change. In short, this paper offers an approach that recognizes both the possibilities and limitations within community-based approaches to refugee service provision.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".