Trends and Issues of Ethnoscience Research from 2008 to 2023: A Bibliometric Analysis
Why this work is in the frame
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Bibliographic record
Abstract
This paper aims to analyze research trends on ethnoscience using bibliometric analysis from 2008-2023. The research sample consisted of 153 documents obtained from the Scopus database. The results of the study show that the distribution of publication frequency reaches its peak in 2021 with 32 articles identified. The distribution of research themes consists of 4 primary clusters and 35 secondary clusters. The ethnoscience research area is dominated by social science research (30.2%). The country with the best documents shows that Indonesia is ranked first as the most productive country in publishing on ethnoscience with 74 identified documents. The United States released second place with 28 documents, third Brazil with 10 documents, fourth Canada with 9 documents, and fifth France, Germany, Italy and the Russian Federation with 5 documents each. Institutions that contributed the most came from Indonesia, Universitas Negeri Semarang 22 papers 33.66%, University of Alberta 9 papers 13.77%, Universitas Negeri Surabaya 7 papers 10.71, Universitas Negeri Padang 7 papers 7.65%. The best author with the highest number of citations is Dahdouh. Meanwhile, if we look at the number of documents published by the author, Sudarmin has 10 documents with a contribution of 15.3%.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.084 | 0.292 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it