Found poetry – Finding home: A qualitative study of homeless immigrant women
Bibliographic record
Abstract
The focus of this article is on the use of found poetry as a tool in qualitative research to examine the experience of precarious housing and homelessness among immigrant women in Montreal. Immigrant and refugee women exhibit greater risk for homelessness than women in general or male newcomers due to higher rates of poverty. Yet little is known about migrant women's experiences of homelessness and less is available from their own perspective, specifically. The article provides a context for understanding female, newcomer homelessness and summarizes the history of the found poem in a variety of disciplines with an emphasis on “social work and the arts” context. This article also details the study methodology and illustrates the process of the found poem technique with two found poems used as data representation. The found poems we present in this article reveal two of the study's key findings related to causes of homelessness: unexpected crises (tipping points) and exploitation.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".