Household water sharing: A review of water gifts, exchanges, and transfers across cultures
Bibliographic record
Abstract
Water sharing offers insight into the everyday and, at times, invisible ties that bind people and households with water and to one another. Water sharing can take many forms, including so-called "pure gifts," balanced exchanges, and negative reciprocity. In this paper, we examine water sharing between households as a culturally-embedded practice that may be both need-based and symbolically meaningful. Drawing on a wide-ranging review of diverse literatures, we describe how households practice water sharing cross-culturally in the context of four livelihood strategies (hunter-gatherer, pastoralist, agricultural, and urban). We then explore how cross-cutting material conditions (risks and costs/benefits, infrastructure and technologies), socio-economic processes (social and political power, water entitlements, ethnicity and gender, territorial sovereignty), and cultural norms (moral economies of water, water ontologies, and religious beliefs) shape water sharing practices. Finally, we identify five new directions for future research on water sharing: conceptualization of water sharing; exploitation and status accumulation through water sharing, biocultural approaches to the health risks and benefits of water sharing, cultural meanings and socio-economic values of waters shared; and water sharing as a way to enact resistance and build alternative economies.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".