Troubled waters: Maori values and ethics for freshwater management and New Zealand's fresh water crisis
Bibliographic record
Abstract
Abstract The landmass of Aotearoa New Zealand totals some 268,000 km2, including 425,000 km of rivers, more than 4,000 lakes and approximately 200 aquifers. For Aotearoa New Zealand's indigenous Maori, these freshwater bodies are part of a complex system of genealogical relationships from which derive the traditional Maori knowledge, values and ethics which shape Maori customary practices for freshwater monitoring and freshwater management. The rupture of these relationships through a century and a half of colonization and industrialization and the dispossession of Maori from their lands and waters also dispossessed Maori of their rights and responsibilities to enact traditional customary practices of kaitiakitanga, stewardship of the natural environment. In 2017 Aotearoa New Zealand's freshwater systems were designated as among the worst in the world. Today they are continuing to degrade. This article focuses on Maori traditional knowledge, ethics and values for freshwater monitoring and management. The article reviews the impact of colonization and development on Aotearoa New Zealand's freshwater systems, the extensive struggle by Maori for recognition of Maori traditional knowledge, rights and responsibilities regarding waterways, and the development of contemporary Maori models for freshwater monitoring and management. Treaty settlements and other legislative initiatives have also catalyzed changes in freshwater management. Faced with catastrophic climatic impacts on freshwater systems and implications for the wellbeing of species and communities, questions of how to ethically manage freshwater are critical. Maori freshwater ethics, values and practices provide a model of renewal and possibility, although one that is not without contest. This article is categorized under: Human Water > Water as Imagined and Represented Human Water > Water Governance Water and Life > Conservation, Management, and Awareness
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".