Data and discrimination: A research note on sexual orientation in the Canadian labour market
Bibliographic record
Abstract
Growing interest in the labour market outcomes of sexual minorities presents novel methodological and theoretical challenges. In this note, we outline important challenges in the study of wage inequality between sexual minorities and heterosexuals in Canada. We discuss the current state of available data on sexual orientation and economic outcomes in Canada, and further evaluate how estimates of sexual orientation wage gaps differ across earnings definition and sample composition. Our analysis of the 2006 Census shows considerable heterogeneity in point estimates of wage disadvantage across definitions of earnings and sample selections; however, all estimates show that gay men suffer labour market penalties and lesbians experience wage premiums.L’intérêt grandissant pour la situation des minorités sexuelles sur marché du travail soulève de nouveaux enjeux méthodologiques et théoriques. Dans ce commentaire, nous soulignons les enjeux importants que présente l’étude des inégalités salariales entre minorités sexuelles et hétérosexuels au Canada. Nous discutons de la disponibilité actuelle de données sur l’orientation sexuelle et le revenu au Canada et évaluons la manière selon laquelle les écarts salariaux varient en fonction de la définition de revenu et la composition de l’échantillon. Notre analyse du recensement de 2006 indique une hétérogénéité considérable des estimations ponctuelles de l’écart salarial à travers différentes définitions de revenu et différentes sélections d’échantillon. Cependant, toutes les estimations indiquent que les hommes gays sont désavantagés sur le marché du travail et que les lesbiennes obtiennent des salaires supérieurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.033 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".