Caring about homelessness: how identity work maintains the stigma of homelessness
Bibliographic record
Abstract
Abstract In this article we interrogate apparently caring statements about homelessness and homeless people for the ways in which they maintain and perpetuate stigmatizing conceptions of homelessness. Drawing on a Foucauldian theoretical framework, we analyze conversations about homelessness gathered in focus groups with members of the general public. Participants used two strategies to construct positive identities for themselves as people who care about homelessness. They describe actions in which they helped specific homeless people, and they describe homeless people as “just like me.” Paradoxically, these statements tap into and reproduce long-standing conceptions of homeless people as culpable for their state, incapable of correcting that state, and in need of proper management and control for their own good. They also divide homeless people into the equally stigmatizing categories of deserving and undeserving poor. Our analysis is in contrast to the traditional literature on stigma, which uses large-scale surveys and experiments to show that negative attitudes, beliefs, and behaviors have stigmatizing potential and assumes that positive attitudes will lead to stigma reduction. We show that caring statements about homelessness and homeless people embed discursive practices that reinforce stigmatizing conceptions of homelessness and maintain existing social inequalities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| 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".