Science Communication Across Diverse Ways of Knowing and Collaboration in a Landscape-Scale Natural Resource Governance Network
Bibliographic record
Abstract
The Northwest Boreal Partnership (“Partnership”), established in 2012 as part of the U.S. Department of the Interior’s Landscape Conservation Cooperative Network, encourages cross-jurisdictional, collaborative natural resources management at a landscape scale. The Partnership is a governance network of Indigenous and non-Indigenous land managers, researchers, and local resource users from a 330-million-acre region of boreal ecosystems in Alaska and northwestern Canada. Central to the purpose of the Partnership are ideas of sharing science information to improve environmental conservation. This case study investigated the relationship between science information and collaboration among diverse participants by drawing on theoretical frameworks related to governance networks, collaboration, and diverse knowledge. Document review, observations, and participant interviews helped characterize the science information shared in the Partnership, its value to participants, and how this relates to collaboration. The analysis highlights themes useful for understanding how the partnership has evolved while maintaining an interest in sharing scientific information: change and uncertainty, scarcity and abundance, and the individual and the whole. These themes provide insights into the complexity of sharing scientific information among participants and the challenges of bringing together diverse ways of knowing that span government, non-profit, Indigenous, and academic settings. Study findings address how the perceived neutrality of science can support participation of a diverse group. Findings also raise questions about whether and how the Partnership creates a base of stability that can sustain trust in a changing natural and political landscape. Lessons from this case may be relevant to other collaborative natural resource management networks with diverse participation.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.020 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".