Justice and injustice in “Modular, Adaptive and Decentralized” (MAD) water systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Centralized water infrastructure is challenged by climate change, infrastructure degradation, underinvestment, and shifting water demands. In its place, scholars have argued for “Modular, Adaptive and Decentralized” (MAD) water systems. We critically interrogate the environmental injustices that produce, and may be reproduced through, MAD water systems. We focus on two key dynamics by which MAD systems emerge: “shoving-out” of, and “opting-out” from, centralized water systems. Using a justice-based framework, we synthesize three cases from Texas, California, and North Carolina, each illustrating how racial and socio-economic marginalization produce MAD water systems. We argue that identifying the structural and relational forces that drive “shove-out” and “opt-out” dynamics remains key for theorizing the enactment of MAD water systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it