Progress in adaptive governance research and hotspot analysis: a global scientometric visualization analysis
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
Adaptive governance has emerged as a prominent theoretical and methodological approach in environmental governance, recognized for its capacity to address evolving conditions and future uncertainties. Despite the extensive literature on adaptive governance since its inception in 2003, a comprehensive review of the literature spanning two decades remains to be conducted. This study addresses that gap by selecting 3274 articles from the Web of Science Core Collection and performing a global scientometric visualization analysis. Our analysis identifies the most productive institutions, authors, journals, publication trends, and research frontiers in adaptive governance research. The findings reveal that there has been a significant acceleration in global research on adaptive governance over the past two decades. Furthermore, the majority of contributions to the field of adaptive governance research have been made by scholars based in the United States, Australia, England, Canada, and the Netherlands. Additionally, existing studies in adaptive governance field focus mainly on subject categories of environmental studies, environmental sciences, and ecology. Finally, the concept of adaptive governance, environmental governance, social-ecological systems, climate change adaptation and social learning were identified as hot topics and emerging trends. This study provides researchers and practitioners with an extensive understanding of the salient research themes, trends, and patterns in global adaptive governance research in an intuitive manner.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.055 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it