Listening Deeply to Indigenous People: A Collaborative Perspective and Reflection Between a Mapuche Machi and Ecologists
Bibliographic record
Abstract
Indigenous Peoples are key knowledge holders and essential partners to confront global environmental crises, especially biodiversity loss. Many calls have been made to better integrate Indigenous Traditional Ecological Knowledge and Western ecological sciences. However, partnerships between these communities are complex due to power imbalances, distrust, different objectives, and injustices towards Indigenous Peoples. This raises the question of what meaningful engagement is, and for whom. These issues were discussed at a scientific workshop in Conguillío National Park, Chile. This initial encounter between ecologists and Mapuche elders, including a Machi (a Mapuche spiritual authority), has led to ongoing dialog and engagement. Responding to calls to listen deeply towards engagement with Indigenous Peoples in Western ecological sciences, we-the Machi and scientists-present our joint perspectives and reflections upon the process, drawing from Indigenous Knowledge and Western ecological sciences. Interweaving both lived experiences and scientific evidence, we document the environmental issues confronting the local Mapuche community caused by industrial developments in the territory. Our joint account highlights conflicts caused by non-native tree plantations and the plans to construct a hydroelectric plant in the Truful-Truful watershed, which was opposed strongly by the local communities. Together with the industrial forestry plantations that cause land-use change, the construction of this hydroelectric plant endangers biodiversity, including species of conservation significance, medicinal plants, and ultimately, the Mapuche way of life. Reflecting upon our collaboration and the process facilitated by Two-Eyed Seeing, we illustrate that Indigenous voices and scientific evidence, together, can deepen our understanding of social-ecological change in the territory and reveal opportunities for building trust and relationships. We highlight the importance of time, preparation for engagement, and advocating for change in knowledge partnerships in the ecological sciences. Learning from our collaboration, we call upon our communities to continue listening, engaging, and advocating for Indigenous representation in ecology.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.036 | 0.037 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".