Managing for resilience
Bibliographic record
Abstract
Abstract Early efforts in wildlife management focused on reducing population variability and maximizing yields of selected species. Later, Aldo Leopold proposed the concept of habitat management as superior to population management, and more recently, ecosystem management, whereby ecological processes are conserved or mimicked, has come into favour. Managing for resilience builds upon these roots, and focuses on maintaining key processes and relationships in social‐ecological systems so that they are robust to a great variety of external or internal perturbations at a range of ecological and social scales. Managing for resilience focuses on system‐level characteristics and processes, and the endurance of system properties in the face of social or ecological surprise. Managing for resilience consists of actively maintaining a diversity of functions and homeostatic feedbacks, steering systems away from thresholds of potential concern, increasing the ability of the system to maintain structuring processes and feedbacks under a wide range of conditions, and increasing the capacity of a system to cope with change through learning and adaptation. The critical aspect of managing for resilience, and therefore ecosystem management, is undertaking adaptive management to reduce uncertainty and actively managing to avoid thresholds in situations where maintaining resilience is desired. Managing adaptively for resilience is the approach best suited for coping with external shocks and surprises given the non‐linear complex dynamics arising from linked social‐ecological systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".