Measurement of gender inequality in neighbourhoods of Québec, Canada
Bibliographic record
Abstract
INTRODUCTION: Few studies have measured gender inequality at levels lower than the country. We sought to develop neighbourhood indicators of gender inequality, and assess their ability to capture variability in gender inequality across Québec, Canada. METHODS: Aggregate 2001 census data for 11,564 neighbourhoods were obtained for men and women. Twelve indicators of gender inequality representing demographic/household characteristics, education, income, work/leisure, and political participation were selected. Neighbourhood-level gender inequality scores were computed for each indicator, and examined across parts of Québec (metropolitan areas, mid-sized cities, rural areas). Monte Carlo simulations were used to assess the ability of indicators to capture heterogeneity in gender inequality across neighbourhoods. RESULTS: Male-dominant neighbourhood-level gender inequality tended to be present for average employment income, labour force participation, employment rate, and employment in managerial positions. Female-dominant gender inequality tended to be present for divorce, single-headed households, and participation in unpaid housework, child and elderly care. Neighbourhood-level gender inequality tended to vary across metropolitan areas, mid-sized cities, and rural areas. Gender inequality scores also varied within these geographic areas. For example, there was greater income-related gender inequality in high than low income neighbourhoods. Monte Carlo simulations suggested that the variation in gender inequality across neighbourhoods was greater than expected with chance alone. CONCLUSION: Neighbourhood-level gender inequality tended to be present in Québec, and varied across parts of the province. Greater awareness of and research on neighbourhood-level gender inequality may be warranted to inform gender policies in Québec and other nations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".