‘My room is like my sanctuary’: Exploring homelessness and home(un)making in the austere city
Bibliographic record
Abstract
Abstract Since austerity policies in the UK began in 2010, homelessness has risen rapidly. Drawing from feminist geographical theories and methodologies, this paper examines experiences of homelessness under austerity in Haringey, London through photo‐elicitation research with one participant, Tessa. This paper argues that home(un)making—the constantly shifting balance of homemaking and unmaking—is central to everyday experiences of, and resistance to, austerity. The paper first demonstrates how Tessa resists austerity through practices of homemaking that enable her to cope with the difficulties of homelessness at a time of austerity. Next, it explores how Tessa's relationships with other actors in the homeless shelter—other residents and government officials—contributed to processes of home‐unmaking, exacerbating the hardships she experiences. By developing the concept of home(un)making, therefore, this paper aims to show the dynamism of home for homeless people under austerity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".