“Somewhere you can go and keep warm and socialise for free”: investigating the impact of the St. Ann’s Warm Space on fuel poverty and social integration
Bibliographic record
Abstract
During the winter of 2022, over 500,000 people visited a warm space in the UK. More than 7,000 warm spaces were set up by voluntary sector and faith groups, libraries, and local authorities to support people facing spiralling fuel prices and the cost-of-living crisis. This report presents the findings of a research project which focuses on a Warm Space established at St. Ann’s Advice Centre, Nottingham, UK. Given the limited knowledge of warm spaces, we aimed to understand the context in which they operate, the experiences of warm space attendees and to explore the role of a service provider. We found that St. Ann’s Warm Space was an example of good practice, which responded to people’s multiple needs in various ways. It provided a refuge from the cold and addressed many of the vulnerabilities individuals were facing related to food and shelter. However, the Warm Space not only provided attendees with useful resources and advice which helped them to physically survive the winter, but also provided emotional support and a supportive community. We discovered that it functioned as an important “social space”, delivering community-based services, and helping to alleviate loneliness and strengthening support networks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".