A Feminist Political Ecology of Where Water “Should” and “Should Not” Be: Insights from Northern Thailand and Amazonian Peru
Bibliographic record
Abstract
In this era of profound environmental change and destructive environmental hazards, much of the media and academic discussion focuses on where water “should not be”—referring to extreme flood events and their implications for social-ecological systems. Challenging normative framings, in this article I focus on culturally and contextually specific human–environment relations to alternatively consider where water “should be” and “should sometimes be.” Specifically, I build on feminist political ecology debates on the everyday dimensions of nature–society relations to uncover individual expectations of natural resources. First, I examine the highlands of northern Thailand, where ethnic Hmong are struggling to maintain viable agricultural production with growing concerns of water scarcity. I draw on contested environmental histories to question where water “should be” in abundance—but is not. Second, I consider Amazonian Peru, where long-standing floodplain settlements rely on the annual flood cycle to sustain livelihoods and riverine lifeways. The increased prevalence of irregularities in the flood pulse, however, are generating new questions for human–environment relations in this setting where water “should sometimes be.” Advancing a lived feminist political ecology approach, I argue that beyond the materialities of water presence (or absence), environmental change is also shaping intimate expectations in human–water relations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".