Routledge Handbook of Gender and Water Governance
Bibliographic record
Abstract
This chapter argues that the relational materiality of water - co-emergent between water's biophysical characteristics and its sociocultural situatedness - impacts the unfolding of gender and livelihood dynamics in specific contexts. Taking the case of semi-arid areas of Maharashtra, India, this chapter examines processes of rural transformation through the lens of a transition between two waters moving across the landscape, probing the resulting shift in gender labor relations. Building on ethnographic fieldwork in two regions, one where livelihoods are dependent on monsoonal rainfalls and another where farmers use wastewater irrigation, we focus on three more-than-human elements at play in this transition: water solutes and sediments, soil organisms and goats. Exploring these as our analytical entry points, we reveal the importance of reconceptualizing labor as a more-than-human relation to better understand how agrarian environments are reshaped by irrigation infrastructure projects. In a feminized agriculture sector, where ecologies are enrolled into processes of agrarian transformation, this perspective problematizes narratives of efficiency behind their stories of successes, demanding metrics that capture the invisibilized work people and their ecologies do together. Further, this work contributes to scholarship on water's materiality, demanding that we engage with the obligations of our more-than-human relatedness to bridge feminist struggles with environmental concerns.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.021 |
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".