Ecosystem services governance: A cross-realm lever for sustainability transformation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it