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Record W3134652992 · doi:10.54656/wyqw9689

Engaging Diverse Audiences: The Role of Community Radio in Rural Climate Change Knowledge Translation

2021· article· en· W3134652992 on OpenAlexaffabout
Abdul‐Rahim Abdulai, Vincent Kuuteryiri Chireh, Roza Tchoukaleyska

Bibliographic record

VenueJournal of Community Engagement and Scholarship · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsMemorial University of NewfoundlandUniversity of British ColumbiaUniversity of Guelph
Fundersnot available
KeywordsCommunity radioRelevance (law)Climate changeKnowledge transferCommunity engagementPublic relationsPolitical scienceSociologyGeographyKnowledge managementComputer scienceEcology

Abstract

fetched live from OpenAlex

Community radio is an important form of knowledge dissemination, especially in rural areas where it can create opportunities for a geographically spread-out audience to engage in local debates. Through this article, we reflect on the community-building function of radio and consider how it can be mobilized to support climate change knowledge transfer in rural communities. Our reflections draw on the use of community radio during the Gros Morne Climate Change Symposium, an event that brought together researchers, practitioners, and community members to discuss coastal climate change adaptation in Newfoundland and Labrador, Canada. We consider the history of radio in Canada, its role in rural communities, and review experiences with radio-focused knowledge dissemination in other locations to frame our own discussion of the topic. Through reflection, each of the co-authors highlights their understanding of the role of community radio at the symposium and argue for the continuing relevance of radio in an era when digital communications are more common. We conclude by arguing that community radio can strengthen place-based identities by creating a distinct forum for engagement and is therefore an important tool for climate change knowledge transfer.

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)0.018
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.701
GPT teacher head0.462
Teacher spread0.239 · 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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Community Engagement and ScholarshipSame topicClimate Change Communication and PerceptionFrench-language works237,207