Enhancing the Ostrom social-ecological system framework through formalization
Bibliographic record
Abstract
Frameworks play an important role in analyzing social-ecological systems (SESs) because they provide shared concepts and variables that enable comparison between and accumulation of knowledge across multiple cases. One prominent SES framework focusing on local resource use has been developed by Elinor Ostrom and her colleagues. This framework is an extensive multi-tier collection of concepts and variables that have demonstrated relevance for explaining outcomes in a large number of case studies in the context of fishery, water, and forestry common-pool resources. The further development of this framework has raised a number of issues related to the formal relationships between the large number of concepts and variables involved. In particular, issues related to criteria for ordering the concepts into tiers, adding new concepts, defining outcomes metrics, and representing dynamics in the framework have been identified. We address these issues by applying methods from research fields that study formal relationships between concepts such as domain-specific languages, knowledge representation, and software engineering. We find that SES frameworks could include the following seven formal components: variables, concepts, attribution relationships, subsumption relationships, process relationships, aggregation relationships, and evaluation metrics. Applying these components to the Ostrom framework and a case study of recreational fishery, we find that they provide clear criteria for structuring concepts into tiers, defining outcome metrics, and representing dynamics. The components identified are generic, and the insights gained from this exercise may also be beneficial for the development of other SES frameworks.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".