Boundary spanners catalyze cultural and prescribed fire in western Canada
Bibliographic record
Abstract
Western Canada is increasingly experiencing impactful and complex wildfire seasons. In response, there are urgent calls to implement prescribed and cultural fire as a key solution to this complex challenge. Unfortunately, there has been limited investment in individuals and organizations that can navigate this complexity and work to implement collaborative solutions across physical, cognitive, and social boundaries. In the wildfire context, these boundaries manifest as jurisdictional silos, a lack of respect for certain forms of knowledge, and a disconnect between knowledge and practice. Here, we highlight the important role of “boundary spanners” in building trust, relationships, and capacity to enable collaboration, including through five case studies from western Canada. As individuals and organizations who actively work across and bridge boundaries between diverse actors and knowledge systems, we believe that boundary spanners can play a key role in supporting proactive wildfire management. Boundary spanning activities include: convening workshops, hosting joint training exercises, supporting knowledge exchange and communities of practice, and creating communication tools and resources. These activities can help overcome unevenly valued knowledge, lack of trust, and outdated policies. We need collaborative approaches to implement prescribed and cultural fire, including a strong foundation for the establishment of boundary spanning individuals and organizations.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".