Contextuality of the strategy of human right to water: Struggle for water access to slum-dwellers in Mumbai, India
Bibliographic record
Abstract
This article responds to the need, highlighted in the academic literature, for in-depth investigations into the role of contextual factors in shaping the struggles for water justice, deploying a strategy that relies on the normative appeal or legitimacy of the human right to water—called the HRW strategy. It demonstrates how specific contextual factors were crucial in influencing the course and outcome of a judicial intervention based on the HRW strategy deployed in the struggle for securing formal water connections to two million slum-dwellers in Mumbai. Although the court upheld the HRW of slum-dwellers and ordered the release of the water connections, the municipal administration promulgated a policy that effectively continued the denial of water access for most of these slum-dwellers. More specifically, the article discusses the strong influence of contextual factors on the initial decision to adopt the HRW strategy and judicial intervention, the success of the legal tactic deployed, and the favorable court order. The article relies mainly on the detailed analysis of formal policy and judicial documents and the data from multiround, semistructured, and extended interviews with 11 respondents who were activists, experts, municipal officials, or media persons.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".