The Utility of Combining the IAD and SES Frameworks
Bibliographic record
Abstract
Elinor Ostrom's IAD and SES frameworks are widely used among social scientists, but each framework suffers from significant problems not shared by the other. The IAD framework lacks detail in terms of the specific social and ecological variables that influence social interactions, resulting in inconsistent applications of a supposedly common framework. The SES framework was designed specifically to resolve that problem, but has lost the dynamic character of the IAD framework. As a result it excels at identifying configurations of social, ecological and institutional factors associated with outcomes, but cannot explain the process by which these factors interact across action situations to generate those outcomes, let alone predict or prescribe changes to social-ecological conditions over time. This article seeks to remedy the problems of each framework by combining them to facilitate detailed and process-oriented studies of social-ecological systems. We then demonstrate the utility of the combined IAD-SES framework by applying it to describe the historical development of Maine's lobster fishery. Future applications of the framework have the potential to address several longstanding questions in the literature on common-pool resources regarding the role of history, power and dynamic social and ecological processes in influencing prospects for environmental sustainability.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".