How Did Environmental Governance Become Complex? Understanding Mutualism Between Environmental NGOs and International Organizations
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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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".