Bibliographic record
Abstract
The literature suggests that there is an existing mismatch between service providers that supply housing and the needs of the most vulnerable and marginalized homeless population, the “hard to house”. This specific population faces complex problematic characteristics like substance use, physical and mental health concerns, particular behaviours (e.g. collecting, aggressiveness), visual appearance, lack of social skills, childhood trauma, working in the sex trade, and so on which leaves them in a constant state of absolute or relative homelessness. To gain a better understanding of their needs, the meaning and creation of home for this population is the focus of this research project. The two main research questions are: What is the meaning of home for those labelled "hard to house"? And how is it created? To answer these questions eight residents at a housing project within Vancouver’s Downtown Eastside have been interviewed, using a naturalistic qualitative research approach. Three emergent themes have been identified: 1) self care, 2) divided neighbourhood, and 3) young adulthood. The themes are discussed by applying Vaclav Havel’s definition of home and integrating Erving Goffman’s theory on interaction rituals, exclusively focusing on two essays “On Face Work” and “The Nature of Deference and Demeanor”.
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 | 0.003 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".