Bibliographic record
Abstract
Abstract Homelessness is a form of extreme poverty characterized by poor health, barriers to accessing health care, and premature death. Homelessness is associated with multiple stigmatized and interlocking identities associated with poverty; substance use and mental illness; disease conditions such as addiction and HIV/AIDS; as well as structural discrimination related to class, gender, age, sexual orientation, and ethnicity. Stigma and discrimination contribute to poor health and the social exclusion of people experiencing poverty and homelessness. Intersectionality is an approach that highlights how multiple identities and structural conditions form interlocking systems of oppression and privilege which benefit some and disadvantage others. Recognizing both constructed and taken‐for‐granted identities highlights the way in which people are positioned in society, especially in relation to policies that constrain choices, structure experiences, and provide access to resources for health and well‐being. Recognizing and challenging multiple constructed identifies and shifting power balances to enhance the social inclusion of people experiencing homelessness is central to addressing the multiple stigmas of homelessness. Structural and systemic discrimination derive from policies that are implicated in the development of homelessness; an understanding of these shifts the responsibility for solving homelessness from individuals to the society in which they live.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".