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Record W4400323895 · doi:10.1139/facets-2023-0109

Boundary spanners catalyze cultural and prescribed fire in western Canada

2024· article· en· W4400323895 on OpenAlexafffundvenueabout
Kira M. Hoffman, Kelsey Copes‐Gerbitz, Sarah Dickson‐Hoyle, Mathieu Bourbonnais, Jodi Axelson, Amy Cardinal Christianson, Lori D. Daniels, Robert W. Gray, Peter Holub, Nicholas A. Mauro, Dinyar Minocher, Dave Pascal

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsCouncil of Yukon First NationsFirst Nations Health and Social Secretariat of ManitobaParks CanadaMinistry of ForestsUniversity of British ColumbiaGovernment of British ColumbiaNative Mental Health Association of Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBoundary (topology)GeographySociologyPolitical scienceMathematics

Abstract

fetched live from OpenAlex

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 These activities can help overcome unevenly valued knowledge, lack of trust, and outdated policies.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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.219
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes4
Has abstractyes

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