Water challenges at the U.S.-Mexico border: learning from community and expert voices
Bibliographic record
Abstract
We discuss the results of a multi-dimensional learning process (expert surveys, community workshops) addressing water challenges at the U.S.-Mexico border. The grand institutional and political framework of the international border, and the tensions and gaps in it, dominates the water literature and expert concerns. However, social inequality and spatial and temporal diversity on both sides of the border emerge as important considerations from community input. Our goal is to make planning for regional water sustainability more comprehensive, both spatially and temporally, and more community responsive in a context of important divisions and inequalities. This is because the “sustainability” frame, as operationalized in resource bureaucracies and academic research, focuses on long-term ecosystem dynamics and supplies of fundamental resources. In this region, however, a supply emphasis on transboundary water quantity hides urgent matters of well-being and justice. For instance, community consultation emphasized two more immediate water issues: water quality, especially microbial issues, and localized catastrophic flooding amid general water scarcity. Understanding how adaptation to environmental change can be pursued efficiently and equitably will require convergent sustainability knowledge and action that addresses multiple sources of risk and potential resilience/adaptation. Framing these within an analysis of social vulnerability can help us to better understand patterns of risk produced by changes in earth systems and act effectively and efficiently to address them in equitable ways. Such a frame is particularly relevant to the U.S.-Mexico border region because of the large vulnerable populations on both sides and comparatively low capacity for collective and household-community resilience on the Mexican side of the border.
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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 | 0.037 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".