Recognition in urban climate justice: marginality and exclusion of migrants in Indian cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the recognitional dimensions of urban climate change justice in a development context. Through the lens of migrants in the Indian cities of Bengaluru and Surat, we highlight how experiences of environmental marginality can be attributed to a lack of recognition of citizenship rights and informal livelihood strategies. Specifically, the drivers of non-recognition in this situation relate to broken social networks and a lack of political voice, as well as heightened exposure to emerging climate risks and economic precariousness. We find that migrants experience extreme forms of climate injustice as they are often invisible to the official state apparatus, or worse, are actively erased from cities through force or discriminatory development policies. Current theories must therefore engage more seriously with issues of recognition to enable more radical climate justice in cities.
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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 | 0.000 | 0.000 |
| 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 it