India, Water and Sustainable Development
Bibliographic record
Abstract
Author’s Note: India faces tremendous developmental challenges, both in water and other sectors, in the next couple of decades. My three-month journey in spring 2008 was, at its core, an exploration of the myriad of complexities to providing safe and sustainable water access. I hope the images will lend an insightful introduction to the challenges of the subcontinent and inspire a desire to learn more. Introduction: In February 2008, I took a break from academic studies for three months of real-life study: trying to understand India’s cultural, social and religious constraints to clean water access. The subcontinent has the second largest population in the world, and nearly a quarter of its peoples are lacking access to safe and reliable water. I set out to discover and document this issue. After landing in Delhi, I traveled to Kanpu, Allahabad and Varanasi, all sites along India’s most famous and-arguably-most toxic river because of heavy domestic and industrial pollution. From there, I went south to the Western Ghats and then northeast to the Kolwan Valley, where stories of complex political dynamics lived in every village. Traveling onward to Mumbai, it was a story of socio-economic inequalities. Finally I ended up in Rajasthan, where the biggest issue is also the most basic: there simply is not nearly enough water. Overall, the journey was an eye-opening exploration of India’s biggest challenge in the coming decade: clean and sustainable water access for all of its citizens. Works Cited: Gupta, Anurag, NGO employee. Interview by author. 4 April 2008. Joshi, Leena. Interview by author. 27 March 2008.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".