Negotiating Indigenous knowledge at the science-policy interface: Insights from the Xáxli’p Community Forest
Bibliographic record
Abstract
• This research generates a framework for understanding knowledge co-production with Indigenous communities. • Some Indigenous communities are effectively engaging in science-policy negotiations by linking knowledge systems. • Linking knowledge systems (including western science and traditional ecological knowledge linkages) occurs a political context. • The analysis proposes using “Indigenous articulations” as a non-reductive framing for Indigenous knowledge linkages. • More inclusive policy-making requires resources for Indigenous communities to create their own knowledge constructions and plans. Despite increasing interest in learning from Indigenous communities, efforts to involve Indigenous knowledge in environmental policy-making are often fraught with contestations over knowledge, values, and interests. Using the co-production of knowledge and social order ( Jasanoff, 2004 ), this case study seeks to understand how some Indigenous communities are engaging in science-policy negotiations by linking traditional ecological knowledge (TEK), western science, and other knowledge systems. The analysis follows twenty years of Indigenous forest management negotiations between the Xáxli’p community and the Ministry of Forests in British Columbia (B.C.), Canada, which resulted in the Xáxli’p Community Forest (XCF). The XCF is an eco-cultural restoration initiative that established an exclusive forest tenure for Xáxli’p over the majority of their aboriginal territory—a political shift that was co-produced with new articulations of Xáxli’p knowledge. This research seeks to understand knowledge co-production with Indigenous communities, and suggests that existing knowledge integration concepts are insufficient to address ongoing challenges with power asymmetries and Indigenous knowledge. Rather, this work proposes interpreting XCF knowledge production strategies through the framework of “Indigenous articulations,” where Indigenous peoples self-determine representations of their identities and interests in a contemporary socio-political context. This work has broader implications for considering how Indigenous knowledge is shaping science-policy negotiations, and vice versa.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.032 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".