The queer immigrant effect: Labour market integration of LGB immigrants in Canada
Bibliographic record
Abstract
Abstract Few have considered whether an immigrant’s sexuality contributes to unique labour market integration and employment outcomes. Using a Canadian immigrant register, consisting of all recently arriving immigrants, and linked income tax records, we break new ground by exploring how biannual arrival cohorts (2000–2010) of lesbian, gay, and bisexual (queer) immigrants fare economically three, five and ten years after arrival. Queer immigrants, who we identify through at least one same-sex tax filing in the first 10 years since arrival, are predominantly arriving from the USA, Europe, and South and Central America as primary economic and family class applicants. They are more highly educated and skilled, less likely to be non-employed, less likely to receive government assistance, and out-earn their heterosexual counterparts over the first 10 years in Canada. Fixed effects modelling reveals a steeper wage growth for queer immigrant men, relative to straight men, between 5 and 10 years since arrival. We also observe the steepest wage growth for straight immigrant women, who enter the labour market with much lower earnings. We posit that queer immigrants leverage social and economic capital from both ethnic and lesbian, gay, bisexual, and transgender communities, aiding in their socio-economic integration in Canada. Our study also highlights important theoretical and empirical considerations concerning the operationalization of sexuality in administrative tax records.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".