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Record W3124241022 · doi:10.1080/17524032.2020.1869576

Place-based Climate Change Communication and Engagement in Canada’s Provincial North: Lessons Learned from Climate Champions

2021· article· en· W3124241022 on OpenAlexafffundabout
Maya Gislason, Lindsay P. Galway, Chris G. Buse, Margot W. Parkes, Emily Rees

Bibliographic record

VenueEnvironmental Communication · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British ColumbiaLakehead UniversityUniversity of Northern British ColumbiaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRuralityClimate changePolitical scienceGeographyEnvironmental resource managementEnvironmental planningPublic relationsRural areaEconomic growthEcology

Abstract

fetched live from OpenAlex

This paper explores how climate change communication is understood and enacted in Canada’s Provincial North (CPN), with a focus on the role that local climate champions play in regions characterized by rurality, remoteness, and a high degree of reliance on natural resource industries. Drawing from 24 in-depth interviews with individuals increasing local attention to climate in Northern British Columbia and Ontario, this research identifies communication challenges and opportunities arising in these contexts. Existing literature inadequately addresses the challenges of advancing climate change initiatives in rural and remote communities. Confirming and extending existing research on place-based communication, CPN climate champions underscored that messages must be place-based, community-informed, reflect local realities, and address the role of industry in regional economies. This paper offers an important set of insights that is relevant to climate change communication in other rural and remote settings in high-income countries.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0230.009
Scholarly communication0.0070.003
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.373
GPT teacher head0.377
Teacher spread0.004 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations40
Published2021
Admission routes3
Has abstractyes

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