Learning in Adaptive Management: Insights from Published Practice
Bibliographic record
Abstract
Adaptive management is often advocated as a solution to understanding and managing complexity in social-ecological systems.Given the centrality of learning in adaptive management, it remains unclear how learning in adaptive management is understood to occur, who learns, what they learn about, and how they learn.We conducted a systematic review using the Thomson Reuters Web of Science, and searched specifically for examples of the practical implementation of adaptive management between 2011 and 2013, i.e., excluding articles that suggested frameworks, models, or recommendations for future action.This provided a subset of 22 papers that were analyzed using five elements: the aims of adaptive management as stated in each paper; the reported achievements of adaptive management; what was learned; who learned; and how they learned.Our results indicate that, although most published adaptive management initiatives aimed at improvements in biological conservation or ecosystem management, scholars of adaptive management tend to report on learning more about governance and about learning, than about ecosystems or biological conservation.Whereas almost all the papers (91%) listed improvements in biological conservation and ecosystem management as aims, 59% reported these as achievements.Whereas only 27% listed improved governance as an aim, 73% mentioned this as an achievement.Conservation scientists and academics reporting on adaptive management tend to learn among themselves, and very seldom (18%) with external stakeholders.Adaptive ecosystem management is dominated by direct assessment and single-loop learning aimed at improving existing practices (86%), with about 50% engaged in double-loop learning and a similar number in deutero-learning (learning about learning).Some adaptive managers (36%) combined double-and single-loop learning and the majority of these (6/8) reported on conservation achievements.A possible explanation for these findings is that adaptive management is an evolutionary process and in most instances is still in an early pioneering stage, possibly held back by participants' capacity for learning.The constraint of learning capacity may also explain why so few adaptive management initiatives reported on learning with societal stakeholders.
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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 | 0.077 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.027 | 0.033 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".