Complex problems and unchallenged solutions: Bringing ecosystem governance to the forefront of the UN sustainable development goals
Bibliographic record
Abstract
Sustainable development aims at addressing economic, social, and environmental concerns, but the current lack of responsive environmental governance hinders progress. Short-term economic development has led to limited actions, unsustainable resource management, and degraded ecosystems. The UN Sustainable Development Goals (SDGs) may continue to fall short of achieving significant progress without a better understanding of how ecosystems contribute to achieving sustainability for all people. Ecosystem governance is an approach that integrates the social and ecological components for improved sustainability and includes principles such as adaptive ecosystem co-management, subsidiarity, and telecoupling framework, as well as principles of democracy and accountability. We explain the importance of ecosystem governance in achieving the SDGs, and suggest some ways to ensure that ecosystem services are meaningfully considered. This paper reflects on how integration of these approaches into policies can enhance the current agenda of sustainability.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".