Bursting bubbles of interiority: exploring space in experiences of distress and rough sleeping for newly homeless people
Bibliographic record
Abstract
Homelessness is an increasing problem in the UK, which intersects in multiple ways with experiences of mental distress. Within the term ‘homeless’ are contained people in a variety of living situations, including those living in temporary accommodation (hostels, couch surfing, B&Bs) as well those sleeping rough. The latter category is the least common, but on the rise. Between 2010 and 2017, rough sleeping more than doubled in England and Wales, with just under a quarter of total rough sleepers concentrated in London (MHCLG, 2018). Loopstra et al. (2016) argue that the combination of recession and austerity has pushed homelessness upwards, with cuts in welfare spending on social care, housing services and income support for older people most clearly associated with this rise. Of new rough sleepers, around 70 per cent have a mental health diagnosis (NHS Confederation, 2012). This is not just a UK phenomenon; a 2009 population based study in the United States similarly found mental health diagnoses to be three to four times more prevalent in the homeless population (Shelton, Taylor, Bonner, & van den Bree, 2009). This relationship is multifaceted. Both mental health problems and homelessness are argued to be inter-related outcomes of lives characterised by adversity, trauma and abuse (Kim, Ford, Howard, & Bradford, 2010). The relationship is also bidirectional; a distress and mental health crisis can lead to people leaving their homes, while homelessness, with its accompanying insecurity and potential for trauma, can also precipitate, deepen or trigger further mental health problems.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".