10. Women and Poverty: The Feminization of Urban Poverty in Canada
Bibliographic record
Abstract
Poverty is disproportionately experienced among men and women. Gender plays a significant role when examining the effects and problems that poverty poses. While poverty can be experienced in differing extremes, it is women who suffer higher poverty rates in almost all societies (Christopher et al.). It is people with disabilities, recent immigrants, and racialized men and women who face additional disadvantages and “all of these groups have extremely high rates of low income and, in all of them, women are the most vulnerable” (Townson). In this paper I discuss how the ‘feminization of poverty’ has created a situation where the number of women in poverty far outnumbers that of men, and how the discourse of feminized poverty is directly affected by the processes and structures of social exclusion. I argue that gender significantly influences the experience and response to urban poverty in Canada through unequal caregiving responsibilities, the dynamics that surround pay inequality, and inadequate government programs.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.045 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".