Resilience Management or Resilient Management? A Political Ecology of Adaptive, Multi-Level Governance
Bibliographic record
Abstract
"Multi-level governance may facilitate learning and adaptation in complex social-ecological circumstances. Such arrangements should connect community-based management with regional/national government- level management, link scientific management and traditional management systems, encourage the sharing of knowledge and information, and promote collaboration and dialogue around management goals and outcomes. Governance innovations of this type can thus build capacity to adapt to change and manage for resilience. However, critical reflection on the emergence of multi-level governance and its many implications for community-based conservation and natural resource management is warranted. Drawing on examples from the North and South, this review examines the challenge inherent in fostering adaptive, multi-level governance and overcoming entrenched management systems. A framework to facilitate analysis is developed by integrating concepts from three complementary bodies of scholarship: common property theory, resilience thinking and political ecology. Core value and attributes of resilience management are identified, and include participation and accountability, leadership, knowledge building learning and trust. However, political ecological interpretations help to reveal the challenge of actualizing those values, and the contextual forces that make entrenched, top-down management systems resilient to change. These forces include the role of power, scale and levels of organization, the positioning of social actors, social constructions of nature and problems confronting governance efforts, knowledge valuation and the roles of ecological systems as agents of social change."
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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".