Nation-building, industrialisation, and spectacle: Political functions of Gujarat’s Narmada pipeline project
Bibliographic record
Abstract
Since 2000 the Indian state of Gujarat has been working to construct a state-wide water grid to connect 75% of its approximately 60 million urban and rural residents to drinking water sourced from the controversial Sardar Sarovar Dam on the Narmada River. This project represents a massive undertaking – it is billed as the largest drinking water project in the world – and is part of a broader predilection toward large, concrete-heavy supply-side solutions to water insecurity across present-day India. This paper tracks the claims and narratives used to promote the project, the political context in which it has emerged, the purposes it serves and, following Ferguson (1990), the functioning of the discursive-bureaucratic 'machine' of which it is a product.The dam’s reinvention as the solution to Gujarat’s drinking water shortfall – increasingly for cities and Special Industrial Regions – reflects a concern with attracting and retaining foreign investment through the creation of so-called 'world-class' infrastructure. At the same time, this reinvention has contributed to a project of nation-building, while remaining cloaked in a discourse of technological neutrality. The heavy infrastructure renders visible Gujarat’s commitment to 'development' even when that promise has yet to be realised for many, while the promise of Narmada water gives Gujarat’s leaders political capital with favoured investors and political supporters. In conclusion, I suggest that the success of infrastructure mega-projects as a political tool is not intrinsically tied to their ability to achieve their technical and social objectives. Instead, the 'spectacle' of ambitious infrastructural development projects may well yield political gains that outweigh, for a time, the real-world costs of their inequity and unsustainability.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.027 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".