The whiteness of ‘safe’ spaces: Developing a conceptual framework to critically examine the well-being of racialized 2SLGBTQ+ people within 2SLGBTQ+ leisure spaces
Bibliographic record
Abstract
2SLGBTQ+ leisure spaces (e.g., 2SLGBTQ+ community centres and recreation groups) offer opportunities to form identities and augment 2SLGBTQ+ people’s overall well-being. These spaces are considered ‘safe’ for 2SLGBTQ+ people to escape heterosexism, while being able to openly express themselves and develop community. However, these might be sites of discrimination for 2SLGBTQ+ people with other minoritized identities (e.g., racialized people), given the whiteness of these spaces. Racialized 2SLGBTQ+ individuals’ experiences of discrimination, generally and within 2SLGBTQ+ leisure spaces, can threaten their well-being, thus highlighting the value of 2SLGBTQ+ spaces, but how do racialized 2SLGBTQ+ people negotiate these often-problematic spaces? This paper presents a conceptual framework that bridges theories and research across social work and leisure studies. The conceptual framework extends the minority stress theory with theories of intersectionality, whiteness, and resilience using a socioecological lens to interrogate experiences and outcomes along multiple dimensions of social identities created by racism and other oppressive systems (e.g., sexism, cisgenderism, classism, ableism) within queer leisure spaces. This paper also describes how the framework can be implemented as an analytic tool and can facilitate investigations of systems of oppression and resilience within queer leisure spaces from the perspective of racialized 2SLGBTQ+ people through critical examination of power relations, relationality, complexity, social justice, and whiteness. Understanding how discrimination occurs and the multi-level resilience-promoting factors that exist in 2SLGBTQ+ leisure spaces will provide an avenue to address the effects of discrimination and foster racialized 2SLGBTQ+ people’s social well-being and inclusion.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".