How Did Environmental Governance Become Complex? Understanding Mutualism Between Environmental NGOs and International Organizations
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
Abstract Recent international relations scholarship has adopted the perspective of organizational ecology (OE) to explore a range of questions related to organizational emergence, strategy, and death. These studies draw attention to organizational competition as the mechanism underpinning important transformations in global governance. We argue that existing work in IR that uses OE has overlooked the importance of another strand of sociological theory that focuses on dynamics of mutualism between organizations. We illustrate the importance of mutualism by focusing on a crucial case: the evolution of different “populations” of organizations working in environmental governance during its critical 1970–1990 period. Our analysis demonstrates that as the environmental consciousness of the 1970s took hold, international non-governmental organizations (INGOs) increasingly captured new resources and stimulated new attention to the issue. Rather than viewing these new actors as competition, existing international organizations (IOs) sought to incorporate and legitimate INGOs, promoting their growth. And in turn, INGOs sought to support and legitimate the activities of the existing IOs, promoting growth of Secretariats and treaties. Our account offers an important organizational-level story that shows that dynamics of mutualism help account for the increased complexity of global governance.
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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