Convergence and divergence of rhetoric and frames in faith-based and secular environmentalism
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".