Prospects for mainstreaming ecosystem goods and services in international policies
Bibliographic record
Abstract
Although most management decisions affecting ecosystem goods and services (EGS) are made at a local level, these local decisions are conditioned by national and international policies. International policy domains provide clear opportunities to mainstream (integrate) EGS in ways that can support poverty reduction. However, positive poverty reduction and EGS outcomes cannot be taken for granted. Mainstreaming EGS needs careful consideration because many of the opportunities identified can reduce poverty, but may have the opposite effect if poorly managed or implemented. A major challenge is to ensure consistent policies across scales and policy domains based on analysis of the local situation. In order to support poverty reduction it matters how the mainstreaming is done and who benefits locally. Based on an analysis of EGS delivery and poverty reduction in drylands, tropical forests and coastal areas in the tropics, this paper analyses the prospects of mainstreaming EGS in a number of relevant international policy domains including: i) development assistance; ii) trade; iii) climate change and; iv) international financial institutions. For these policy domains it is analyzed how mainstreaming EGS can contribute to reaching poverty reduction and development goals, what relevant policy tracks for mainstreaming EGS exist, and what priority issues should mainstreaming focus on. The paper next provides an overview of possible tools and mechanisms for mainstreaming and ends with conclusions on what the role of the CBD can be in mainstreaming.
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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 | 0.032 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".