Socioeconomic Disparities: Exploring the Intersection of Gender and ‘Ruralness’ in the Canadian Labour Market
Bibliographic record
Abstract
Employment earnings is a highly intersectional and important labour market outcome, with a virtually endless list of interacting forces dictating individual experiences. Gender and geographic location are known to shape employment earnings separately. In this study, I explore the interaction of both gender and spatial location in terms of ‘urbanness’ and ‘ruralness’—finding that while individuals tend to earn less in more rural regions, it is in the more urban zones that gendered earnings gaps are the greatest. Additionally, I consider whether participation in paid employment versus self-employment is influenced by gender and ‘urbanness’ or ‘ruralness’. Using descriptive statistics, Ordinary Least Squared regression analysis, and the influence of intersectionality theory, I seek to understand the relationship between gender and spatial location as they relate to employment earnings and the class of worker. This research paper was produced as the final capping project (SOC 900b Directed Research Project) for the Department of Sociology’s course-based Master’s program, completed in January 2024.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".