Transdisciplinary science for improved conservation outcomes
Bibliographic record
Abstract
Summary Major advances in biology and ecology have sharpened our understanding of what the goals of biodiversity conservation might be, but less progress has been made on how to achieve conservation in the complex, multi-sectoral world of human affairs. The failure to deliver conservation outcomes is especially severe in the rapidly changing landscapes of tropical low-income countries. We describe five techniques we have used to complement and strengthen long-term attempts to achieve conservation outcomes in the landscapes and seascapes of such regions; these are complex social-ecological systems shaped by interactions between biological, ecological and physical features mediated by the actions of people. Conservation outcomes occur as a result of human decisions and the governance arrangements that guide change. However, much conservation science in these countries is not rooted in a deep understanding of how these social-ecological systems work and what really determines the behaviour of the people whose decisions shape the future of landscapes. We describe five scientific practices that we have found to be effective in building relationships with actors in landscapes and influencing their behaviour in ways that reconcile conservation and development. We have used open-ended inductive enquiry, theories of change, simulation models, network analysis and multi-criteria analysis. These techniques are all widely known and well tested, but seldom figure in externally funded conservation projects. We have used these techniques to complement and strengthen existing interventions of international conservation agencies. These five techniques have proven effective in achieving deeper understanding of context, engagement with all stakeholders, negotiation of shared goals and continuous learning and adaptation.
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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 | 0.035 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".