Collaborative governance in forestry issues: A bibliometric analysis with VOS viewer software using Scopus database
Bibliographic record
Abstract
This study aims to analyse scientific literature on collaborative governance in forestry issues published from 2001 to 2022. To ensure paper quality, the study will use VOSviewer software to visualise the bibliometric analysis from Scopus Database. Bibliometric analysis was used to analyse 72 papers about collaborative governance in forestry issues. VOSviewer software visualised publishing trends, country/institution/author contributions, journal distribution, highly cited articles, bibliographic coupling analysis, and keyword analysis. According to the study, collaborative governance in forestry publications has increased considerably in the last decade. Most research on this issue comes from the US, Canada, and Australia. Colorado State campus was the study's most affiliated campus, followed by Saskatchewan and Oregon. This study was published in Land Use Policy, Society and Natural Resources, and Ecology and Society. The combination of bibliographies and keyword concurrency networks showed that collaborative governance in forestry issues is closely linked to sustainable development, environmental governance, and forest governance. The study finds that collaborative governance in forestry issues needs more research, especially with collaborative governance as a framework. The bibliometric analysis provides a complete overview of publishing trends, country/institution/author contributions, journal distribution and highly cited articles, bibliographic coupling analysis, and keyword analysis in this field. Researchers, policymakers, and practitioners interested in collaborative governance in forestry may profit from the study. The results may identify key contributors, influential journals, and critical study areas linked to this topic.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.024 | 0.203 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".