The social, the ecological, and the adaptive. Von Bertalanffy's general systems theory and the adaptive governance of social‐ecological systems
Bibliographic record
Abstract
Abstract Based on biological insights, Ludwig von Bertalanffy coined general systems theory (GST) and later expanded his perspective, exploring what GST could mean for other disciplines and other types of systems. We make a case for the relevance, or rather, the importance, of GST for coming to a new understanding of the resilience of social‐ecological systems and the possible forms of adaptive governance that might increase such resilience. After analyzing the conceptual structure of the resilience paradigm and of GST, we identify concepts in resilience thinking where GST provides new confirmation or modifies the perspective: complexity, evolution, self‐organization, and adaptation. We discuss post‐Bertalanffy developments in the interdisciplinary and twinned fields of systems theory and complexity studies that can provide bridging concepts between GST and resilience thinking. In conclusion, we emphasize the need for both cognitive and institutional resilience to foster adaptive governance. We highlight the management of couplings between systems and the switching between forms of understanding and forms of organization, where self‐organization and more centralized forms of steering can alternate and combine.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".