"And We Still Ain't Satisfied": A Report on Gender and Income Inequality in Canada
Bibliographic record
Abstract
The gender gap is much wider than is commonly believed.This analysis of Statistics Canada's 1998 Survey of Labour and Income Dynamics finds that women's median, after-tax incomes were 61 percent of men's, while 50 percent of women in Canada had after-tax incomes ranging from zero to $ 13,786.The gender gap was greatest for women aged 46-64, placing many women in this cohort at risk of poverty in their senior years.Women's prevalence in part-time and temporary employment, continuing occupational segregation and wage discrimination keep women's incomes low.Unionization and university education are the best ways for women to raise their incomes and close the gender gap.RESUME Le fosse des sexes est beaucoup plus large qu'on ne le pense.Cette analyse de Statistiques Canada sur la dynamique entre le travail et le revenu, de 1998, rapporte que la mediane de revenu pour les femmes, apres taxes, etait 61 pour cent de celles des hommes, tandis que 50 pour cent des femmes au Canada, avaient des revenus entre zero et 13, 786$.Le fosse entre les sexes etait plus large pour les femmes agees entre 46 et 64 ans, placant un bon nombre de femmes dans cette cohorte de personnes a risque d'etre pauvre durant leur vieillesse.La prevalence de femmes qui ont des emplois a temps partiel et des emplois temporaires continue la segregation et la discrimination salariale qui fait que le revenu le revenu des femmes reste bas.La syndicalisation professionnelle et les etudes universitaires sont les meilleurs moyens pour les femmes de hausser leurs revenus et de reduire l'ecart entre les sexes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".