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.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.212 | 0.254 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".