Governance of ecosystem services: a review of empirical literature
Bibliographic record
Abstract
Although researchers have postulated different modes of governance, the degree of empirical support for different governance modes in ecosystem service literature remains unclear. Understanding the contexts under which governance modes have been researched and applied in practice could help decision-makers choose appropriate strategies to the provision of ecosystem services. We conducted a literature review to explore the development of empirical research on ecosystem services governance and to illustrate research frontiers and gaps in this research. We reviewed 157 empirical papers on the governance of ecosystem services published between 2006 and 2019. Our results show that the number of papers about the governance of ecosystem services has increased and that researchers have mainly used qualitative and mixed methods. No governance mode has dominated the research field. Rather, different governance modes have been studied in combination, possibly reflecting the fact that multiple and overlapping governance arrangements often affect the provision of ecosystem services. The geographical distribution of ecosystem services governance research is diverse, but misses perspectives from certain regions, such as Southeast Asia. This means that while decision-makers in well-studied areas like Western Europe can use a pool on studied arrangements, in other areas decision-makers may find limited literature to inform their decisions to maintain and strengthen ecosystem services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".