Water Back: A Review Centering Rematriation and Indigenous Water Research Sovereignty
Bibliographic record
Abstract
The recent Land Back movement has catalysed global solidarity towards addressing the oppression and dispossession of Indigenous Peoples’ Lands and territories. Largely absent from the discourse, however, is a discussion of the alienation of Indigenous Peoples from Water by settler-colonial states. Some Indigenous Water Protectors argue that there cannot be Land Back without Water Back. In response to this emergent movement of Water Back, this review of research by Indigenous and non-Indigenous writers traces the discursive patterns of Indigenous Water relationships and rematriation across themes of colonialism, climate change, justice, health, rights, responsibilities, governance and cosmology. It advances a holistic conceptualization of Water Back as a framework for future research sovereignty, focusing mainly on instances in Canada, Australia, Aotearoa New Zealand, and the United States. We present the findings on the current global Waterscape of Indigenous-led research on Indigenous Water issues. Water Back offers an important framework centring Indigenous ways of knowing, doing, and being as a foundation for advancing Indigenous Water research. © THIS ARTICLE IS DISTRIBUTED UNDER THE TERMS OF THE CREATIVE COMMONS ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE LICENSE WHICH PERMITS ANY NON COMMERCIAL USE, DISTRIBUTION, AND REPRODUCTION IN ANY MEDIUM, PROVIDED THE ORIGINAL AUTHOR(S) AND SOURCE ARE CREDITED. SEE HTTPS://CREATIVECOMMONS.ORG/LICENSES/BY-NC-SA/3.0/FR/DEED.EN
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".