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Record W4296196472 · doi:10.1002/pan3.10399

Integrating Indigenous and scientific perspectives on environmental changes: Insights from boreal landscapes

2022· article· en· W4296196472 on OpenAlexafffundabout
Annie Claude Bélisle, Sylvie Gauthier, Hugo Asselin

Bibliographic record

VenuePeople and Nature · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversité du Québec en Abitibi-Témiscamingue
FundersFonds de recherche du Québec – Nature et technologiesSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsIndigenousTraditional knowledgeClimate changeEnvironmental resource managementGeographyEnvironmental planningPolitical scienceEnvironmental ethicsEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Major environmental changes affect the health and capacity of ecosystems to sustain Indigenous people's well‐being in boreal landscapes. Collaboration between Indigenous communities and researchers could help assessing and mitigating the consequences of environmental changes. We used Driver Pressure State Impact (DPSI) conceptual models to compare the perspectives of Indigenous and scientific communities on environmental changes in boreal landscapes of Quebec, Canada. The Indigenous DPSI model emerged from interviews with local land‐use experts from two Indigenous communities. The scientific model was informed by the publication topics of expert researchers. We compared the Indigenous and scientific models and exposed convergences and divergences between perspectives. Forestry was identified as a major driver of change in both models. Most issues related to mining, hydro‐power and forest road development were specific to the Indigenous model. Climate change and wildfires were of greater interest in the scientific model. Convergences between the perspectives of Indigenous and scientific communities are conducive to collaborative research. Divergences could be addressed through reciprocal knowledge transfer activities, which would lead to research that better aligns with the concerns and needs of Indigenous communities. Read the free Plain Language Summary for this article on the Journal blog.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.992

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 studies0.0090.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.279
Teacher spread0.271 · 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.

Study designQualitative
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

Citations15
Published2022
Admission routes3
Has abstractyes

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