There is no word for ‘nature’ in our language: rethinking nature-based solutions from the perspective of Indigenous Peoples located in Canada
Bibliographic record
Abstract
Abstract Support for nature-based solutions (NbS) has grown significantly in the last 5 years. At the same time, recognition for the role of Indigenous Peoples in advancing ‘life-enhancing’ climate solutions has also increased. Despite this rapid growth, the exploration of the intersection of NbS and Indigenous Peoples has been much slower, as questions remain about the ability of NbS to be implemented while respecting Indigenous rights, governance, and knowledge systems, including in their conceptualizations. To address this knowledge gap, we draw on 17 conversational interviews with Indigenous leaders, including youth, women, technicians, and knowledge keepers from what is currently known as Canada to explore Indigenous conceptualizations of nature, nature-based solutions, and the joint biodiversity and climate crisis. Three drivers of the biodiversity and climate crisis were identified: structural legacy of colonization and capitalism, a problem of human values, and climate change as a cumulative impact from industrial disturbances. Building on this understanding, our findings indicate that shifting towards Indigenous conceptualizations of NbS as systems of reciprocal relationships would: challenge the dichotomization of humans and nature; emphasize the inseparability of land, water, and identity; internalize the principle of humility and responsibility; and invest in the revitalization of Indigenous knowledge systems. As the first exploration of Indigenous conceptualizations of nature within NbS literatures, we close with four reflections for academics, advocates, leaders, activists, and policymakers to uplift Indigenous climate solutions for a just, equitable, and resilient future.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.063 | 0.041 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| 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".