Finding Work in the Age of LGBTQ + Equalities: Labor Market Experiences of Queer and Trans Workers in Deindustrializing Cities
Bibliographic record
Abstract
Despite legal protections and growing acceptance in many industrialized countries, LGBTQ + workers continue to face considerable employment disadvantage. We explain this contradiction by detailing labor market processes that limit employment prospects for LGBTQ + workers in Sudbury and Windsor (both small cities with industrial histories). Drawing on 50 semistructured interviews and 662 community survey responses from LGBTQ + workers, we show how LGBTQ + employment opportunities are constrained by a constellation of multiscalar factors. These include the absence of good work opportunities outside of blue-collar work in deindustrializing labor markets, associated persistent cisnormativity and heteronormativity, and inconsistent protection from discrimination and social acceptance at work. As a result, respondents self-selected out of blue-collar workplaces, avoided and left jobs when they experienced or anticipated discrimination, and chose to remain in jobs with supportive employers rather than find a new job in a potentially homophobic or transphobic labor market. This article extends current understandings of labor markets economic geography by connecting production histories to persistent cisnormativity and heteronormitivity, and by showing how the search for emotional safety in cities with inconsistent social acceptance perpetuates economic disadvantage for queer and trans workers.
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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 | 0.001 | 0.002 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 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".