The missing markets link in global-to-local-to-global analyses of biodiversity and ecosystem services
Bibliographic record
Abstract
While the impacts of global drivers such as international trade, population growth, technological development or climate change on local-level pricing, decision making, biodiversity and ecosystem services (BES) have received strong and increasing attention over the recent decades, relatively few studies have examined how impacts on local BES due to human activities or how local responses targeted to improve BES outcomes, can propagate to regional, national and global scale. We discuss the challenges that frequently arise in global-to-local-to-global frameworks when modelling policies aimed at improving land-use change while also maximising the associated benefits from the state of biodiversity and the provision of ecosystem services. We present four complexities associated with case studies that describe approaches to protecting BES in diverse landscapes and contexts within the proposed framework: heterogeneity in local markets; additionality; spillover and leakage effects; and unintended consequences. Our study calls for filling these gaps in our understanding through interdisciplinary, open-source research characterizing the local-to-global biodiversity and ecosystem services linkages in future.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".