Perceptions of Participants and Stakeholders of a ‘Sleepout’ Event Held to Raise Money for, and Awareness of, Homelessness Charity Work
Bibliographic record
Abstract
t_ Fundraising events have become a dominant platform for charitiesin raising money to deliver services for vulnerable population groups. ‘Sleepout’events are unique, whereby participants spend one night in a sleeping bag orcardboard shelter, raising awareness and money for homelessness charities.These events have become increasingly popular, particularly in the UK, US,Canada, and Australia. The present study documents evidence from, as far ascan be ascertained, the first study to explore the perceptions of participantsregarding sleepout events, including staff from housing and homelessnessservices, and people with lived experienced of sleeping on the street. Whilstmost participants had a favourable view of these events in raising awarenessand funds for charity, there was, however, a degree of ambivalence aboutsleeping outside for one night as the vehicle for fundraising. Many recognisedthat a single, safely organised outdoor event does not replicate the experiences of street-based sleeping. Therefore, some support staff expressed avery strong and visceral dislike of these events as misleading and distasteful.Recommendations were made that more consideration should be given toeducation and awareness raising during the events to ensure participantsunderstand more of the complexity of the issues surrounding homelessness,and the most effective evidence-based solutions
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".