Navigating the City and the Workplace: Migrant Female Construction Workers and Urban (Im)Mobilities
Bibliographic record
Abstract
While their labour shapes the growing cityscape, migrant construction workers often remain invisible – not only to property developers and consumers but also to the state. For female workers, this is compounded by gender-based discrimination within the industry. Utilising ethnographic data, this article explores how women working in construction in Bengaluru, India, both experience and strive for mobility. It provides a multi-sited analysis to establish the ways in which intersectionality between employment conditions, the urban environment and gender norms may inhibit or facilitate urban mobility for migrant female workers. Few ethnographic studies have attended to women’s experiences of intermingled work/accommodation sites within the industry, although the practices and outcomes produced by the blurring of such boundaries provides fertile ground for analysis. While the article confirms the enduring nature of discrimination experienced by women in the construction industry, it also attends to the ways in which female workers were able to utilise spaces of exploitation. I conclude that precarious livelihoods may not at first glance yield enduring or substantive beneficial outcomes for those compelled to undertake them, but they are nevertheless productive – allowing for the maintenance and fulfilment of aspirations which may not reside within the urban domain. KEYWORDS circular migration; labour; gender; women; construction work
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".