Resilience and the Co-Evolution of Ecosystems and Institutions
Bibliographic record
Abstract
"Resilience is the ability of a system to cope with change without collapsing. It is the capacity to absorb external perturbations, by actively adapting to an ever changing environment. Reduction in resilience means that vulnerability increases, with the risk that the whole system flips from one equilibrium state to another. Such flips are often a consequence of the misuse of the environment and the inertia of institutions to change. Smaller unpredictable perturbations that previously could be handled turn into major crises when extreme events intersect with internally generated vulnerability due to loss of resilience. To avoid such situations there is a need for institutions with the ability to respond to and manage environmental feedbacks, institutions that can cope with unpredictable perturbations before they accumulate and challenge the existence of the whole social-ecological system. This implies that it is not enough to only understand the institution in question. The dynamics of the ecosystems that form the biophysical precondition for the existence of the institution need to be taken into account as well. This study focuses on the linked social-ecological system, and its dynamic interrelationships. We regard it as one system with its social and ecological components co-evolving over time. It is in this context that we study traditional and newly-emergent social-ecological systems. We are analyzing 1) how the local social system has adapted to and developed a knowledge system for dealing with the dynamics of the ecosystem(s) including the resources and services that it generates, 2) specifically, how the local system maintains ecosystem resilience in the face of perturbations, and 3) those combinations of property rights arrangements, institutions, and knowledge systems which accomplish the above successfully. Examples will be presented from the Cree Indians of the Canadian eastern subartic and their resource management, and pastoral herders and rangeland management in semi-arid Africa."
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".