Red, White, and Blue: Environmental Distress among Water Stakeholders in a U.S. Farming Community
Bibliographic record
Abstract
Abstract This paper explores environmental distress (e.g., feeling blue) in a politically conservative (“red”) and predominantly white farming community in the southwestern United States. In such communities across the United States, expressed concern over environmental change—including climate change—tends to be lower. This is understood to have a palliative effect that reduces feelings of ecoanxiety. Using an emotional geographies framework, our study identifies the forms of everyday emotional expressions related to water and environmental change in the context of a vulnerable rural agricultural community in central Arizona. Drawing on long-term participant-observation and stakeholder research, we use data from individual (n = 48) and group (n = 8) interviews with water stakeholders to explore reports of sadness and fear over environmental change using an emotion-focused text analysis. We find that this distress is related to social and material changes related to environmental change rather than to environmental change itself. We discuss implications for research on emotional geographies for understanding reactions to environmental change and uncertainty.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".