Gendered Space in a Smart City: Traversing Women's Experiences in the Informal Sector
Notice bibliographique
Résumé
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 Published and unpublished papers, reports, newspaper articles were secondary data.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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».