Gender Segregation and the Gender Wage Gap: Rising Inequality in Alberta and Saskatchewan
Bibliographic record
Abstract
Canada has made significant historical commitments towards implementing gender equality policies, programs, and legislation after the findings of the Abella Commission were released in 1984 (Abella, 1984). Since then, gender wage gaps have been decreasing in many parts of the country. Two of the Canadian prairie provinces, Alberta and Saskatchewan, have not experienced the same degree of measureable gender equity at the national level, as the other provinces. This paper will therefore examine the gender wage gaps in these two provinces. To investigate gender wage gaps in Alberta and Saskatchewan, the paper examines two industries in each of the two provinces that are fundamentally different in terms of the gender of their workforce composition. In particular, the study compares the oil and gas industry, which is predominantly male dominant, with the predominantly female dominant healthcare and social assistance industry in order to discover whether wage gaps are industry specific, and can be explained by the size of worker-affiliated organizations in particular provinces and industries. The study also investigates the effects of years of job tenure on gender wage gaps. The results demonstrate that, in both provinces, regardless of industry, the size of the organization proves significant in explaining gender wage gaps, while years of tenure are insignificant. this study showed that students who have learned through the E-book method achieve design efficiently better in their post-test scores than those in the traditional method. Students at the internal motivation level perform design efficiently better in their post-test scores than those at external motivation level. The E-book method proved to help students with external motivation in their post-test score motivation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".