Othering homelessness: how lone mothers manage housing insecurity in Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract: Homelessness is a complex socio-economic and political problem that impacts health status and continues to challenge researchers and policy makers in Canada. Individuals and families who lack safe, affordable shelter are heterogeneous and represent almost every facet of society. Although living in poverty is a common denominator for those who experience homelessness, researchers report multiple contributors to housing insecurity including poor mental health, addiction, family violence, unemployment, and insufficient wages. Many of these contributors are understood to be consequences of individual circumstances rather than stemming from much broader socio-economic and political conditions.Research that has explored homelessness in Canada has predominantly been conducted in large urban areas. However, in Prince Edward Island, a province with a high percentage of rural residents, there is no research that has examined housing instability among any population, including lone parent mothers. This knowledge gap is a critical omission concerning women, especially for those who live in rural Canada as there is limited understanding of their daily challenges. The research question for this study was: How do lone parent mothers manage their lives while living homeless in Prince Edward Island? The methodology used was constructivist grounded theory. Similar to classic grounded theory, constructivist grounded theory aims to create a theoretical understanding of the basic social processes at play that are contributing to a social phenomenon, and how participants respond. The purpose of this doctoral study was to create a theoretical understanding of how and why homelessness among lone parent mothers in PEI is a problematic social-economic problem and how they managed in response to their circumstances.Fourteen lone mothers who had experienced homelessness were interviewed at a place and time of their choosing. Following ethics approval, data generation and analysis began immediately and continued in an iterative process until no further discernments were identified. By understanding the lone mothers’ behaviours in response to their socio-ecological circumstances, a social process became apparent which culminated in behaviours of othering the experience of homelessness. A theory titled Othering Homelessness: How Lone Mothers Manage Housing Insecurity in Prince Edward Island, Canada was created based on data analysis to explain this social process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".