Bibliographic record
Abstract
Indigenous Peoples are struggling for water justice across the globe. These struggles stem from centuries-long, ongoing colonial legacies and hold profound significance for Indigenous Peoples’ socioeconomic development, cultural identity, and political autonomy and external relations within nation-states. Ultimately, Indigenous Peoples’ right to self- determination is implicated. Growing out of a symposium hosted by the University of Colorado Law School and the Native American Rights Fund in June 2016, this Article expounds the concept of “indigenous water justice” and advocates for its realization in three major trans- boundary river basins: the Colorado (U.S./Mexico), Columbia (Canada/U.S.), and Murray-Darling (Australia). The Article begins with a novel conceptualization of indigenous water justice rooted in the historic United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP)—specifically, UNDRIP’s foundational principle of self-determination. In turn, the Article offers overviews of the basins and narrative accounts of enduring water-justice struggles experienced by Indigenous Peoples therein. Finally, the Article synthesizes commonalities evident from the indigenous water-justice struggles by introducing and deconstructing the concept of “water colonialism.” Against this backdrop, the Article revisits UNDRIP to articulate principles and prescriptions aimed at prospectively realizing indigenous water justice in the basins and around the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".