Ecosystem services governance: A cross-realm lever for sustainability transformation
Bibliographic record
Abstract
The leverage points perspective is used increasingly in sustainability transformation research. Scholars have proposed three realms of leverage for the sustainability research agenda: human-environment interactions; institutional dynamics, and sustainability-related knowledge creation and use. However, studies aiming to better understand the role of cross-realm levers, which create parallel change in human-nature interactions, institutions, and knowledge production and use, remain scarce. To address this research gap, we provide an Ecosystem Services Governance (hereafter, ESGov 1 ) lens to conceptually and empirically investigate the potential cross-realm lever role of ESGov for sustainability transformations. Through theoretical and empirical analyses we: 1) identify the key features within the three sustainability transformation realms and analyze how ESGov can shape and influence them; 2) test the potential for ESGov to be a cross-realm lever for sustainability transformation using a case study from Agua Blanca (Ecuador); 3) navigate intra-realm dynamics, identifying features from diverse realms that may simultaneously be fostered by ESGov; and, 4) ultimately, contribute to the transformation, ecosystem services, and governance literatures by highlighting the enabling mechanisms within ESGov that can facilitate cross-realm sustainability transformation interventions. This study reveals that ESGov, when configured to embrace relational thinking, collaborative governance, and inclusive knowledge integration, can effectively serve as a cross-realm lever. Ultimately, we advocate for a shift from Ecosystem Services to ESGov framework as a means to catalyze cross-realm interventions, advancing sustainability through a nuanced understanding and designing of the dynamic interplay between society and the environment.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".