A bibliometric review of stakeholders' participation in sustainable forest management
Bibliographic record
Abstract
Although stakeholders' participation in forest management helps overcome problems and conflicts that prevent sustainable solutions, different approaches and nomenclature for similar contents in the literature hinder theoretical progress on the topic. This study organises existing information through a bibliometric analysis of scientific papers from the last 30 years (1991–2021) on sustainable forest management, focusing on the stakeholders' participation. Results demonstrate that stakeholders' participation in sustainable forest management gained relevance from 2017 onwards. Case studies are predominant (66%) and six major trends were identified. The first emphasises a systemic approach to participation. The second updates the community management discussion. The third studies historical problems related to the use of resources, rights, and services. The fourth focuses on regional assessments and studies. The fifth concerns assessment, decision-making, and planning, including issues related to certifications and policies. The sixth discusses innovation related to adaptation, climate change, equity, and resilience. The studies included in this last classification are problem-solving-oriented and seek new forest management. Although important, the role of innovation in stakeholders' participation in sustainable forest management is overlooked, which constitutes an avenue for future research.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.118 | 0.179 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".