Integrating Conservation and Sustainable Development Through Adaptive Co-management in UNESCO Biosphere Reserves
Bibliographic record
Abstract
Integrating conservation and sustainable development is difficult, but organisations charged with this mandate must move forward with implementation. Adaptive Co-Management (ACM), an approach that brings together the learning function of adaptation with the linking function of collaboration, has been identified as a promising way to enhance the effectiveness of sustainable development without compromising conservation efforts. We examine four UNESCO Biosphere Reserves (BRs) to better understand the extent to which they exhibit characteristics of ACM integrated conservation and sustainable development and gain insights into how they do so. We find that the BRs we studied in Canada and Sweden undertake a substantial number of activities strongly oriented towards integration of conservation and development objectives. These activities involve a wide variety of actors in both on-the-ground implementation efforts and decision-making activities, create novel spaces for interaction among participants which contributes to their bridging ability, and draw on social networks, available assets and individuals' contributions to enable actions in pursuit of their integrative mandate. Insights into these activities and how they were undertaken can offer lessons for future practice and research within the World Biosphere Reserve Network, as well as conservation organisations more broadly. Although we demonstrate that significant efforts are being made towards integration of conservation and development, we nonetheless suggest that further studies should explicitly investigate if and how such integration actually lead to more desirable social and ecological outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".