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Record W4404089829 · doi:10.1080/23251042.2024.2419329

Convergence and divergence of rhetoric and frames in faith-based and secular environmentalism

2024· article· en· W4404089829 on OpenAlexaffabout
Tanhum Yoreh, Grace King

Bibliographic record

VenueEnvironmental Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmentalismRhetoricFaithDivergence (linguistics)Convergence (economics)Environmental sociologySociologySecularismSocial scienceEnvironmental ethicsPolitical scienceEpistemologyPhilosophyPoliticsLawTheologyLinguistics

Abstract

fetched live from OpenAlex

A true shift to living within the carrying capacity of the planet requires widespread collaboration across all sectors of society. Coalitions are built on a foundation of shared values and common goals. This study is a step towards understanding the language and values that make environmental non-governmental organizations (ENGOs) and religious (faith-based) environmental NGOs (RENGOs) unique, and what language and values have the potential to bring these parallel movements together. There has been a long-standing distinction between the moral language of religious environmentalism and the technocratic and policy-oriented language of secular environmentalism in the Northern Hemisphere. To assess differences and commonalities between the language of secular and religious activists, we apply framing theory and rhetoric theory to 81 mandate statements from ENGOs and RENGOs in the USA, UK, and Canada. We observe that ENGOs are more likely to reference ‘nature’ and ‘wilderness’ while RENGOs prefer ‘creation,’ ‘care,’ and ‘justice.’ The groups differed again over ‘sustainability’ discourse: ENGOs discussed sustainability as a policy, while RENGOs were concerned with sustaining relations. The movements, however, share the keywords ‘people,’ ‘community,’ and ‘life,’ indicating potential alignment around the frames of human wellbeing and community responsibility.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.999

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 studies0.0000.003
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 head0.012
GPT teacher head0.271
Teacher spread0.260 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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