“…full of opportunities, but not for everyone”: A narrative inquiry into mechanisms of labor market inequity among precariously employed gay, bisexual, and queer men
Bibliographic record
Abstract
BACKGROUND: This study brings lesbian, gay, bisexual, transgender (trans), and queer (LGBTQ+) populations into scholarly discourse related to precarious employment through a political economy of queer struggle. METHODS: Drawing on narrative inquiry, 20 gay, bisexual, and queer men shared stories of precarious employment that were analyzed using Polkinghorne's narrative analysis. RESULTS: Results tell an overarching narrative in three parts that follow the trajectory of participants' early life experiences, entering the labor market and being precariously employed. Part 1: Devaluation of LGBTQ+ identities and adverse life experiences impacted participants' abilities to plan their careers and complete postsecondary education. Part 2: Participants experienced restricted opportunities due to safety concerns and learned to navigate white, cis, straight, Canadian ideals that are valued in the labor market. Part 3: Participants were without protections to respond to hostile treatment for fear of losing their employment. CONCLUSIONS: These stories of precarious employment illustrate unique ways that LGBTQ+ people might be particularly susceptible to exploitative labor markets.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".