There is no word for ‘nature’ in our language: rethinking nature-based solutions from the perspective of Indigenous Peoples located in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it