Adaptive learning in natural resource management : three approaches to research
Bibliographic record
Abstract
This paper explores different approaches to applied research in natural resource management that focus on adaptive learning as an element of the resource management challenge of continuous sustainable production. The research frameworks suggested by Adaptive Management (AM), social learning, and complex adaptive systems (resilience thinking) are considered. While AM typically emphasizes natural science and ecological systems, and social learning emphasizes human agency and interaction, resilience thinking addresses socialecological systems as complex entities that behave in dynamic and cyclical fashion. All three frameworks offer insights into practices that support learning, adaptation, and sustainability. Some of the experience in the Canadian province of British Columbia is given in example. The emerging framework of adaptive comanagement offers a promising approach to capturing relevant features of the other three. These four different conceptual approaches should not be seen as mutually exclusive alternatives but rather are characterized by overlapping features with different focal strengths. To date, experience with applying any of these frameworks in practice is limited and remains a big challenge. The conceptual frameworks considered here could underpin research into more effective adaptive learning in resource management. RPE Working Paper Series ii Paper 22: Stephen R. Tyler
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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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.005 | 0.082 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".