Trends and Issues of Ethnoscience Research from 2008 to 2023: A Bibliometric Analysis
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%.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.086 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".