Bibliographic record
Abstract
The conditions of homelessness pose a barrier to the health and well-being of people experiencing homelessness (PEH), which is especially true during extreme weather events. As the homelessness crisis worsens, existing service providers find it challenging to address the issue of homelessness and reduce risk during extreme weather events. I conducted a qualitative case study of homeless governance and extreme weather response in Regina, Saskatchewan, by conducting 22 semi-structured interviews with local government officials, healthcare and outreach workers, housing and shelter staff members, and community workers and advocates. The following questions guided my study: How have responses to extreme weather and homelessness been successful, and what are the challenges faced? How does governance affect people experiencing homelessness and how does it relate to risk during extreme weather? Why is it difficult to address the issues of homelessness and extreme weather risk? The main challenges in supporting PEH pertain to issues of capacity, coordination, the changing nature of provision, the depoliticization of services, and the individualization of responses. I find that regimes of governance in Regina utilize punitive, caring, and disciplinary measures that can exclude PEH from accessing safe shelter spaces, increasing risk during extreme weather. I apply a critical realist meta-theory and use the concept of neoliberal governmentality to theorize how the limitations of service provision and the conditions of marginalization PEH face are attributed to the neoliberal capitalist settler colonial structure. Therefore, solutions must directly address oppressive structures responsible for the crisis. Key words: Homelessness; Homeless Governance; Extreme Weather; Extreme Weather Response; Governmentality; Settler Colonialism; Neoliberalism
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".