Research Trend of Socioscientific Issues Based on Scopus Journal Database: A Bibliometric Study from 2011 to 2021
Bibliographic record
Abstract
The implementation of socioscientific issues in science learning has increased recently. The purpose of this study is to highlight research trends over the previous ten years by examining the findings of bibliometric papers on socio-scientific issues. A total of 648 articles from the English-language Scopus database were analyzed using the VOS Viewer. The results of the analysis reveal that studies related to socio-scientific issues over the last ten years are still an increasing research trend. Keywords related to socio-scientific issues such as argumentation, decision making, scientific literacy, critical thinking, and climate change. The journal sources that were most cited were the international journal of science education, journal of research in science teaching, research in science education, international journal of science and mathematics education, science and education. Articles that are widely cited by other authors are Sadler T.D, Zeidler, D.L, Osborn, J, Eilks, I, Erduran, S, Lederman, N.G, Simon, S. Leading countries in the field of socio-scientific issues are the United States, Germany, Sweden, Taiwan, Australia, Turkey, United Kingdom, Canada, Spain, Indonesia. Further researchers can conduct scientific studies on socio-scientific issues by using educational technology in the form of digital media and other variables that have not been studied or are still little researched.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.101 | 0.163 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".