O que sabemos sobre a governança ambiental em áreas protegidas? : Tendências e direcionamentos
Bibliographic record
Abstract
Environmental governance can be defined as the interaction between structures, processes, and traditions that determine how power and responsibilities are exercised by stakeholders, for which various governance instruments can be used. Its use has grown in recent years, especially in protected areas. Here, our objective was to analyze the articles indexed in two databases – Web of Science and Scopus – on environmental governance in protected areas, to establish an overview of the studies worldwide. For this, scientometrics, a methodology linked to metrics, can trace scientific advances over time. Data were processed in software RStudio and Excel. Chloropleth maps were also prepared in the program QGIS 3.22.16. The study is divided into a literature review; study design, data collection, analysis, visualization, and interpretation. The search time interval was 1999-2022. As a result, 768 articles were in the two databases, distributed in 222 journals, being carried out by 2555 authors. The USA, United Kingdom, Australia, Canada, Brazil, Mexico, and Indonesia were the countries that most stood out in scientific production. As expected, most studies fall within the governance and methodological categories. The results indicate that the use of scientometric analysis proved to be important in identifying and monitoring research on environmental governance in protected areas. The evaluation of the knowledge produced is a necessity for all sectors that carry out research, as it is possible to infer new areas of investigation, as well as to determine and point out gaps in knowledge.
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 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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".