Environmental non-governmental organizations and global environmental discourse
Bibliographic record
Abstract
Environmental non-governmental organizations (ENGOs) exist worldwide, and since the 1980s they have increasingly influenced global environmental politics and environmental discourse. We analyze an original dataset of 679 ENGOs participating in global environmental conventions in the mid-2010s, and we apply quantitative content analysis to ENGO mission statements to produce an inductive typology of global environmental discourse. Discourse categories are combined with ENGO attribute data to visualize the political topology of this globally-networked ENGO sector. Our results confirm some common assertions and provide new insights. ENGOs are more diverse than conventionally recognized. Quantitative evidence confirms strong North-South disparities in human and financial resources. Four primary discourses are identified: Environmental Management, Climate Politics, Environmental Justice, and Ecological Modernization. We compare our typology to existing literature, where Climate Politics and Environmental Justice are under-appreciated, and we discuss ways to expand on the data and methods of this study. Synoptic empirical ENGO research is essential to accurately understanding the ENGO sector and global environmental politics.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".