Social enterprises as spaces of encounter for mental health consumers
Bibliographic record
Abstract
Recent geographical scholarship has suggested that disabled people's encounters with non‐disabled others might constitute an important mechanism for community participation and social inclusion. Existing studies recognise the importance of the immediate material and relational character of place in shaping the nature of encounters between disabled and non‐disabled people. Yet there has been little attention – beyond questions of physical access – to the ways in which places might be practically created to facilitate positive interactions. In this paper, we examine the potential of social enterprises to serve as a space of encounter between disabled workers and non‐disabled others. Drawing on interviews and focus groups, we explore how and to what extent social enterprises (coffee shops, catering businesses, cleaning and janitorial companies, courier services) create supportive environments for interaction and encounter between workers with mental health problems, clients and members of the public. Our analysis suggests that specific efforts to accommodate and support mental health consumers in their employment‐based interactions contributes to an enabling relational space for encounters that begin to unsettle prevailing assumptions about mental ill health. At the same time, broader social conditions and enduring assumptions about mental health consumers work to reinforce disabling differences.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".