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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".