Bibliographic record
Abstract
I illustrate through this paper how contemporary water (in)justice results from interactions between historical, socio-political, technical, and economic relations, and how such water (in)justice is emotionally experienced and embodied. Focusing on the case of Faizalpur, a low-income Muslim neighborhood in segregated Ahmedabad, I draw on lived experiences approaches to water justice and on an emotional political ecology framework to offer a multi-scalar analysis set across urban, community, and individual scales. I show how the settlement of low-income Muslim families in Faizalpur is inseparable from both the (il)legal status of this land and the religious segregation that have shaped this city. In turn, everyday experiences of water injustice in Faizalpur are premised in contestations relating to the site's land use zoning history. I illustrate how in this contested site carefully framed requests make municipal water infrastructure possible even though such infrastructure is technically disallowed here. The careful-ness of such requests lies in skirting issues pertaining to (il)legality, instead activating other discursive categories, such as ‘humanitarian’ need: categories that possess the moral power to outweigh legal and technical arguments. I suggest that everyday experiences of water (in)justice cannot be understood without attending to the discursive power of planning terms like ‘illegality’ and ‘land use zoning’. Emotionally experienced everyday water struggles in Faizalpur, in the form of anger, trust, fear, grief, etc., need to be understood then as emotional everyday experiences of religious segregation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".