Our Shared Relationship with Land and Water: Perspectives from the Mayangna and the Anishinaabe
Bibliographic record
Abstract
Abstract The health and well-being of Indigenous communities are deeply connected to respect for the rights of nature. This relationship is informed by their unique knowledge systems which view the world as an interdependent unity of beings with their environment. Drawing on concepts from ecopsychology, interviews with Indigenous peoples and Elders in the Bosawas Reserve (Nicaragua) and the Kettle and Stony Point First Nation (Canada), and qualitative research conducted by the authors, this paper presents a comparative case study analysis of the experience of Mayangna communities around issues of forest protection and Anishinaabe perspectives on water. For the Mayangna peoples, the health of their communities is intrinsically connected to respect for Mother Earth. They struggle for enforcement of existing laws to prevent the deforestation of the Bosawas Reserve, home to their communities and to diverse ecosystems. Similarly, for First Nations peoples in Canada, water, the lifeblood of Mother Earth, is essential for the preservation of a traditional and contemporary way of life. For many of these communities, a lack of water security is a persistent problem which influences all aspects of life. Codified in Indigenous knowledge systems and taught by First Nations Elders are teachings for balanced relationships with creation—behavior necessary for survival and to ensure sustainability for future generations. These cases point to the need for a decolonizing framework to resource management, calling for holistic and participatory perspectives to governance, and recognition of spiritual and cultural relationships with nature.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".