A systematic review and thematic synthesis of Canada’s LGBTQ2S+ employment, labour market and earnings literature
Bibliographic record
Abstract
BACKGROUND: The last two decades have witnessed a considerable growth in the literature focusing on LGBTQ2S+ employment, labour market inequality, and income. During the same period, Canada has emerged as a trailblazer in employment protections for both sexual and gender minorities. Unfortunately, the Canadian literature on LGBTQ2S+ employment outcomes and experiences is disperse and underdeveloped. OBJECTIVE: This paper brings together this disperse research and provides the first systematic review of Canada's LGBTQ2S+ employment and earnings literature. METHODS: We start with a systematic review and thematic synthesis of the broadly defined literature on LGBTQ2S+ poverty in Canada. We use a thematic synthesis to isolate the LGBTQ2S+ literature on employment, labour market inequality, and earnings. Our search of electronic databases took place in April 2018 and was updated in January 2019. RESULTS: A total of 532 abstracts and full texts were screened by reviewers, which resulted in 84 articles included in our final sample. These articles were then sorted by keywords and those pertaining to employment, labour market inequality, and income (n = 31) were included in this analysis. While estimates of sexual minority wage gaps vary depending on the data and methods used, most studies have found wage penalties for gay men and wage premiums for lesbians, relative to their heterosexual counterparts. The literature on bisexual employment is particularly scant but finds that bisexual men and women also earn less than their heterosexual counterparts. Research on the subjective workplace experiences of LGBTQ2S+ individuals find unique challenges, barriers and, at times, exclusion from the Canadian labour market. CONCLUSIONS AND IMPLICATIONS: While the literature on LGBTQ2S+ employment outcomes and experiences in Canada is growing, much is left unknown. The principal limitation for researchers continues to be the dearth of population-based surveys that include questions on sexual orientation, gender identity, and relevant employment characteristics. To date, few studies have explored employment outcomes or the subjective workplace experiences of bisexuals, transgender, two-spirit or other gender minority peoples.
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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 | 0.040 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.043 | 0.048 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".