Beyond Shelters: Unpacking Global Lessons from Scotland’s Journey Towards Housing Justice
Bibliographic record
Abstract
Globally, shelters dominate accommodation-based responses to homelessness, offering dormitory-style ‘shared air’ accommodation at a significant scale in many cities, with specific rules on length of stay, curfews, and behaviour. Despite their widespread use, shelters are associated with negative experiences and a range of harms. Based on this evidence, this paper argues that reducing shelter use in favour of less damaging alternatives is necessary to the pursuit of housing justice, by which we mean minimising housing-related harms to the very worst off. The paper’s key contribution is to demonstrate that ending shelter use is an achievable policy goal, as demonstrated in Scotland, where a shelter-free response to homelessness was in operation from 2020 to 2024. Drawing on data from a longitudinal study of trends in and responses to homelessness across Great Britain, we identify the factors that enabled the closure of shelters in response to the coronavirus (COVID-19) pandemic - including Scotland’s comparatively low levels of rough sleeping - and the immediate steps that were taken to do so. We also assess the sustainability of Scotland maintaining a shelter-free homelessness response and key risks to it, including rising demand, gaps in provision, and public opinion/community action. The paper closes with a discussion of the implications for homelessness policy and practice globally and the potential for a paradigm shift away from shelter-based responses.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".