Between inclusion and disconnection: LGBTQ Workers and the challenge of union renewal
Bibliographic record
Abstract
Over the past three decades, unions have played a critical role in advancing the rights and equality of lesbian, gay, bisexual, transgender, and queer (LGBTQ) people and other equity-seeking groups within and outside of unions. Accordingly, most unions in industrialized countries have institutionalized equity internally and through external campaigns, often delivered by union centrals. Scholarship about these initiatives has been largely celebratory, arguing that they have rendered unions more representative and socially just. This paper draws on interview and survey responses to explore LGBTQ union members' experiences and perceptions of their unions in two deindustrializing cities. Workers who participated in union LGBTQ initiatives felt protected by their unions, less isolated in the workplace and more connected to coworkers and their unions. More commonly, however, workers faced barriers to accessing LGBTQ programming, felt disconnected from their unions and preferred employer to union support. These findings qualify the assumption that upscaling LGBTQ initiatives will advance union renewal. Instead, we suggest that strategies that foster inclusion in the workplace and union local are more likely to promote union engagement among, and social justice for, LGBTQ workers, particularly in places without preexisting support for LGBTQ people.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".