Migrating injustices in the small city: drought-impacted interstate migrant workers’ experiences in Tiruppur’s sanitation sector
Bibliographic record
Abstract
Climate stresses like droughts amplify economic precarity, pushing low-income, caste-oppressed communities across India to pursue temporary rural-urban migration as an adaptation strategy. This paper develops an intersectional framework on ‘migrating injustices’ to examine how caste and regional identity mediate interstate rural-urban migrants’ experiences of vulnerabilities and injustices, particularly as these experiences move and endure with migration, shaping migrants’ adaptive abilities. The framework is applied to trace the injustices that a group of 800-odd drought-impacted, caste-oppressed, landless persons from central India experience through their adverse economic incorporation as sanitation workers in Tiruppur’s privatized sanitation sector in southern India. Findings reveal that a history of caste oppression and uneven development in their home region produced unequal drought impacts for our informants, pushing them to migrate. However, migrants’ caste and regional identity combine with their climate and economic precarity to direct their migration into precarious, unjust working conditions and employer-provided, environmentally risky accommodations—both removed from local socio-political networks, undermining migrants’ ability to contest injustices in Tiruppur. In highlighting the translocal and trans-sectoral intersections between the migrating environmental, economic, and caste-based injustices for circular migrants, the paper argues that eliminating migrating injustices is crucial for achieving transformative adaptation and urban climate justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".