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Preparing fisher folk for Climate Change: Communication Strategies

2012· article· en· W12962645 on OpenAlexaboutno aff
Swathi Lekshmi

Bibliographic record

VenueMarine Environmental Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodClimate changeAdaptation (eye)Climate change adaptationOrder (exchange)Political scienceEnvironmental resource managementEnvironmental planningPublic relationsBusinessGeographyPsychologyAgricultureEcologyEconomics

Abstract

fetched live from OpenAlex

Climate change has become a worldwide concern, increasingly impacting the livelihoods of individuals in both the global north and south. The need to develop effective adaptation and mitigation strategies in the south has become crucial to securing livelihoods and community development. A critical element in promoting effective and successful adaptation and mitigation strategies is communication. Originally presented as a complex and abstract scientific problem, climate change information is now shared and discussed among various disciplines and stakeholders. Effective communication among stakeholders can help to identify problems, raise awareness, encourage dialogue, and influence behavioural change (Johnson 2011; Moser 2010; Nerlich, Koteyko& Brown 2010). However, in order to communicate climate change effectively, it is important to understand and acknowledge how individuals and communities think about, interpret, and discuss the causes, issues, and possible adaptation and mitigation actions (Africa Talks Climate, BBC World Service Trust 2010).

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0390.008

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 head0.065
GPT teacher head0.338
Teacher spread0.274 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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