Walking on two legs: a pathway of Indigenous restoration and reconciliation in fire‐adapted landscapes
Bibliographic record
Abstract
Worldwide, Indigenous peoples are leading the revitalization of their/our cultures through the restoration of ecosystems in which they are embedded, including in response to increasing “megafires.” Concurrently, growing Indigenous‐led movements are calling for governments to implement Indigenous rights, titles and treaties, and many settler‐colonial governments are committing to reconciliation with Indigenous peoples and to implementing the United Nations Declaration on the Rights of Indigenous Peoples. Yet, despite growing recognition that just and effective conservation is only possible through partnerships with, or led by, Indigenous peoples, decolonizing approaches to restoration have received insufficient attention. However, reconciliation will be incomplete without Indigenous‐led restoration of Indigenous lands, knowledges, and cultures. In this article, we introduce the concept of “walking on two legs” to guide restoration scientists and practitioners in advancing the interconnected processes of Indigenous‐led restoration and reconciliation in Indigenous territories. As an action‐oriented framework articulated by Secwépemc Elder Ronald E. Ignace, “walking on two legs” seeks to bring Indigenous knowledges into balance with western scientific knowledge in service of upholding an Indigenous stewardship ethic that is embedded in Indigenous ways of relating to land and embodies principles of respect, reciprocity, and responsibility. Grounding this discussion in the context of fire‐adapted ecosystems of western Canada and unceded and traditional Secwépemc territory, Secwepemcúl̓ecw, we argue that walking on two legs, along with principles of reconciliation, offers a pathway to uphold respectful relationships with Indigenous peoples, knowledges, and territories through Indigenous‐led restoration.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.069 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".