Bibliographic record
Abstract
SUMMARY How can the governance of environment and resources be devolved in a way that incorporates effective user participation and feedback learning? Approaches that use the idea of adaptive management or learning-by-doing, combined with co-management, are particularly promising. Using an interdisciplinary literature covering many types of resources, and a conceptual model with three phases (communicative action, self-organization and collective action), the paper identifies some of the major processes leading to adaptive co-management. These include deliberation, visioning, building social capital, trust and institutions, capacity-building through networks and partnerships, and action-reflection-action loops for social learning. Such adaptive co-management is not simply a theoretical possibility but something that has been documented in a number of forestry, fisheries, wildlife, protected area, and wetland cases from both developed and developing countries. However, the experience with the decentralization reforms of the 1990s is largely negative for a number of reasons. Effective devolution takes time, requiring a shift in focus from a static concept of management to a dynamic concept of governance shaped by interactions, feedback learning and adaptation over time. Sharing of governance responsibilities and an ability to learn from experience are among the emerging trends in environmental management.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".