Evolutionary Study of Watershed Governance Research: A Bibliometric Analysis
Bibliographic record
Abstract
This study aims to analyze scientific literatures on watershed governance from the first published paper (1979) to the present (2020) through bibliometric analysis and visualization by utilizing a VOS-viewer software based on Scopus database. This study retrieved 353 articles from international authors focusing on watershed governance topic that are related to rural-urban linkages, and local government capacity. The articles are classified according to year of publication, author, the country of co-authors, affiliation, keywords, and journal title. Furthermore, the articles are examined based on several indicators including: Contribution of Countries/Institutions/Authors, Distribution of Journals, Highly Cited Articles, Bibliographic Coupling, and Keywords Analysis. The United States, Canada, and the United Kingdom are the leading countries contributing to publications on watershed governance topic from 1979 to 2020. McGill University serves as the most productive institution, followed by Ohio State University and University of British Columbia. Meanwhile, in terms of disciplines, Public Administration and Development, Environment and Urbanization, and Public Policy and Administration are the top three published journals. The combination of bibliographies and keyword concurrency networks indicates that the research topic of watershed governance is strongly associated to research topics such as rural-urban linkages and local government.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.159 | 0.210 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".