Inclusion or Exclusion?: Gendered Experiences and Strategies of Migrants in Informal Settlements in Bengaluru
Bibliographic record
Abstract
Internal migration, nearly four times more than the migration across national boundaries, accounts for the largest human movements in the world. In context of agrarian distress, circular migration between the rural and the urban is a common livelihood trajectory of at least 100 million Indians. It is known that circular migrants face numerous economic and social challenges of survival in the city. This article focuses on gendered experiences and strategies adopted by migrants for inclusion in informal settlements in Bengaluru. Drawing on the concepts of social exclusion, inclusion and agency, we use household surveys ( n = 1,109) in 30 informal settlements in Bengaluru in 2016, semi-structured interviews ( n = 20) in one informal settlement, key informant interviews ( n = 5) and participant observation in events and meetings in the city to illustrate individual and collective strategies used by diverse groups and the ways in which these are gendered. We find that the length of stay in the settlement is a crucial determinant of social inclusion in the city.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".