Ecosystem Services and Poverty Alleviation (OPEN ACCESS)
Bibliographic record
Abstract
Understanding how to sustain the services that ecosystems provide in support of human wellbeing is an active and growing research area. This book provides a state-of-the-art review of current thinking on the links between ecosystem services and poverty alleviation. In part it showcases the key findings of the Ecosystem Services for Poverty Alleviation (ESPA) programme, which has funded over 120 research projects in more than 50 countries since 2010. ESPA’s goal is to ensure that ecosystems are being sustainably managed in a way that contributes to poverty alleviation as well as to inclusive and sustainable growth. As governments across the world map how they will achieve the 17 ambitious Sustainable Development Goals, most of which have poverty alleviation, wellbeing and sustainable environmental management at their heart, ESPA’s findings have never been more timely and relevant. The book synthesises the headline messages and compelling evidence to address the questions at the heart of ecosystems and wellbeing research. The authors, all leading specialists, address the evolving framings and contexts for the work, review the impacts of ongoing drivers of change, present new ways to achieve sustainable wellbeing, equity, diversity, and resilience, and evaluate the potential contributions from conservation projects, payment schemes, and novel governance approaches across scales from local to national and international. The cross-cutting, thematic chapters challenge conventional wisdom in some areas, and validate new methods and approaches for sustainable development in others. The book will provide a rich and important reference source for advanced students, researchers and policy-makers in ecology, environmental studies, ecological economics and sustainable development. The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9780429016295, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.191 | 0.060 |
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".