Does the domestic space belong to women? An Assessment of the Housing in the New Indian Urban Agenda through the lens of gender
Bibliographic record
Abstract
Households in the cultural context of a patriarchal society such as India, are primary headed by men. The reason why a female heads a household is not because of improving social and economic status of women—it is unlikely that a woman will be considered head of the household in the presence of her husband. It is mostly because there is no alternative (Masoodi, 2015). According to the 2011 Census, about 27 million households in India (11% of total households in the country), are headed by women. Still the socio cultural system places women as a outsider in their own family - the one who will marry and leave her parents' house for her husband - and as the outsider in their husband's house who came into the family through the marriage. In spite of it all, the house remains at the heart of their lives. It is where they spend most of their time, look after their family and children and even run businesses. In such a conflicting scenario of ownership and belonging to the house, it is essential that we question our housing policies, building and property ownership regulations for their adequacy of providing safety and security to the women. This paper presents India's current scenario through the lens of gender, with focus on India's new urban agenda and the Sustainable Development Goals. The purpose is to highlight the gaps in the system which weaken women's position as an equal member of the society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".