Governing large-scale social-ecological systems: Lessons from five cases
Bibliographic record
Abstract
This paper compares lessons drawn from five case studies of large scale governance of common-pool resources: management of forests in Indonesia, the Great Barrier Reef in Australia, the Rhine River in western Europe, the Ozone layer (i.e. the Montreal Protocol), and the Atlantic Bluefin Tuna (i.e. the International Convention on the Conservation of Atlantic Tuna). The goal is to assess the applicability of Ostrom’s design principles for sustainable resource governance to large scale systems, as well as to examine other important variables that may determine success in large scale systems. While we find support for some of Ostrom’s design principles (boundaries, monitoring, sanctions, fit to conditions, and conflict resolution mechanisms are all supported), other principles have only moderate to weak support. In particular, recognition of rights to organize and the accountability of monitors to resource users were not supported. We argue that these differences are the result of differences between small and large scale systems. At large scales, other kinds of political dynamics, including the role of scientists and civil society organizations, appear to play key roles. Other variables emphasized in common-pool resource studies, such as levels of dependence on resources, group size, heterogeneity, disturbances, and resource characteristics also receive mixed support, pointing to the need to reinterpret the meaning of common-pool resource theories in order for them to be applicable at larger scales.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".