The Benefits of the Everyone In Initiative and the Deeper-Rooted Problems It Revealed for Migrants Experiencing Homelessness
Bibliographic record
Abstract
This article addresses the broad research aim of understanding migrants’ experiences of homelessness during the Coronavirus Disease of (COVID-19) pandemic through a novel combination of linguistic and sociological analysis. In our analysis of life story interviews, we find that the United Kingdom (UK) Government’s Everyone In initiative, which suspended eligibility criteria to provide support and accommodation to those experiencing homelessness or deemed to be at risk of rough sleeping, was hugely beneficial for migrants. This indicates what is possible when there is the political will to end rough sleeping. In its analysis of life stories gathered during the pandemic, the article proceeds to identify deeper-rooted problems relating to the weak and restricted structural position of migrants experiencing homelessness. Having spent time in the UK with an ‘inferior status’, with limited access to work and welfare, economic and social capital, and often with experiences of trauma in the UK and/or in their countries of origin, many of our research participants express a lack of control and a sense of being controlled in their conditions of existence. Further, their isolation and loneliness in the individual rooms provided in the emergency accommodation is indicative of a deeper-rooted sense of separation deriving from years spent sleeping rough or living in temporary and insecure accommodation. Experiences of isolation and the sense of a lack of control are corrosive to mental health, and during the pandemic, mental health problems were also exacerbated by welfare checks and other rule-based practices that are potentially re-traumatising.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".