Gendered Space in a Smart City: Traversing Women's Experiences in the Informal Sector
Bibliographic record
Abstract
Scholars find Indian cities are not gender-sensitive in urban planning, making women residing in these cities vulnerable to violence. However, the Smart Cities Mission in India, which started in 2015, specifically aimed to facilitate safe and liveable cities, especially for women. In the current scenario, migrants from widely varying backgrounds are moving into the cities that are part of the Smart City Mission. Currently arrival cities that are part of the mission are seeing an increase in low-wage labour migrants, high-status expatriates, student migrants, and marriage migrants. However, reports and studies reflect a higher rate of vulnerability of poor female migrant workers to violence and exploitation in their workplace than men or other non-migrant female workers. In this context, it is essential to understand the safety and security of poor migrant women who work in the informal sector and reside in these smart cities. There is also a dearth of empirical studies that consider the gender perspective of smart city initiatives in different locales. Thus, this chapter aims to contribute to the urbanisation and gender analysis of urban infrastructure, particularly of smart city, Bhubaneswar. Urban sociology is used for the theoretical understanding of the context. For the study, migrant women working in the informal sector and their experiences of infrastructural and institutional support were considered. Data was collected from both primary and secondary sources on migrant women’s experiences of smart cities. Published and unpublished papers, reports, newspaper articles were secondary data. For primary data, respondents from seven wards from different municipality zones of the city were selected. For this, the zonal map of Bhubaneswar was used, and a total of 100 respondents from each zone (north, south-east, and south-west) were selected for the study. Respondents were chosen through the snowball sampling method. Moreover, both interstate and interdistrict migrants were considered for the study. The interview schedule was used to collect data, and content analysis was used to analyse it. The data show that the majority of migrant women in the study lived in places where they had access to basic facilities like streetlights, proper roads, and police night patrols. Further, the study found that physical infrastructure and usage of space, such as the built environment, play an essential role in making the city a safe space for women.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".