Homeism: Naming the Stigmatization and Discrimination of Persons Experiencing Homelessness
Bibliographic record
Abstract
We examined stigmatization and discrimination experienced during the process of hospital discharge by people with lived experience of homelessness (PWLEs). We propose the term “homeism” as the discrimination (behaviour) towards an individual who is homeless; this form of discrimination is the result of negative stereotypes (stigmas) towards individuals who are experiencing homelessness. Based on a qualitative secondary data analysis of interviews with 20 shelter/housing and healthcare providers and 20 PWLEs, we identified four categories related to homeism: 1) who stigmatizes PWLEs and where stigmatization and discrimination occur, 2) reasons why PWLEs experience stigmatization and discrimination, 3) outcomes of stigmatization and discrimination, and 4) recommendations to reduce or eliminate stigma and discrimination. We propose a conceptual model that depicts the processes of homeism, including precursors, experiences, and outcomes. By naming homeism, we aim to instigate housing activism and future scholarship on this phenomenon to be pursued alongside interventions aimed at eliminating homeism.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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 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".