Practitioner Perceptions of Adaptive Management Implementation in the United States
Bibliographic record
Abstract
Adaptive management is a growing trend within environment and natural resource management efforts in the United States.While many proponents of adaptive management emphasize the need for collaborative, iterative governance processes to facilitate adaptive management, legal scholars note that current legal requirements and processes in the United States often make it difficult to provide the necessary institutional support and flexibility for successful adaptive management implementation.Our research explores this potential disconnect between adaptive management theory and practice by interviewing practitioners in the field.We conducted a survey of individuals associated with the Collaborative Adaptive Management Network (CAMNet), a nongovernmental organization that promotes adaptive management and facilitates in its implementation.The survey was sent via email to the 144 participants who attended CAMNet Rendezvous during 2007-2011 and yielded 48 responses.We found that practitioners do feel hampered by legal and institutional constraints: > 70% of respondents not only believed that constraints exist, they could specifically name one or more examples of a legal constraint on their work implementing adaptive management.At the same time, we found that practitioners are generally optimistic about the potential for institutional reform.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".