Environmental non-governmental organizations and global environmental discourse
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it