Why women stay poor: An examination of urban poverty in India
Bibliographic record
Abstract
In this paper the relationship of women to poverty in urban areas is explored and the need to understand the gender dimension of poverty in a specific cultural context is emphasised. In recent years there has been an increasing trend to incorporate the gender dimension in the analysis of poverty. The féminisation of poverty is a term used to describe the overwhelming representation of women among the poor. The present study examines the gender bias of poverty which underlies the social and economic subordination of women and the effects of gender on access to basic amenities such as education, health care and labour force participation. The 1996 World Bank publication, Poverty Reduction and the World Bank, identified three components to urban poverty: 1) provision of basic services such as water, sanitation, drainage and roads; 2) taking action on the top threats to health (lead, dust and microbial diseases); 3) making municipal finance more businesslike and inclusive. While these are commendable objectives, the problems of urban poverty for women can be examined in a qualitative way from the point view of how these goals are absorbed into the social and cultural surroundings of the urban poor. Why women are more vulnerable to poverty will be considered here and how the causes and experience of poverty differ by gender are determined, followed by some remarks on how to alleviate women's poverty.
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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 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".