Trends and Issues in Science Education in the New Millennium: A Bibliometric Analysis of the JRST
Bibliographic record
Abstract
As a real time socio-scientific issue, the COVID-19 pandemic has clearly shown us the need for the public to understand science. As experts have repeatedly stressed in recent years, science education plays an important role in developing scientifically literate societies. In this context, it is critical to consider which subjects science educators frequently concentrate on and the messages they give to researchers, policymakers, and other stakeholders. Therefore, the purpose of this study was to use bibliometric data to understand the topics that the articles in the Journal of Research in Science Teaching (JRST), one of the flagship journals about science education and teaching, focused on over the last 20 years. This study employed both descriptive and bibliometric analysis. Based on data from the Web of Science (WoS), descriptive analyses are presented as frequencies and percentages and we used VOSviewer software for bibliometric analysis. Findings showed that more than 80% of the authors of the JRST are from the United States, Australia, Canada, and the United Kingdom. Moreover, results of these analyses demonstrate that the researchers publishing in the JRST focused on two main ideas over the past 20 years: “Which science teaching methods and strategies are most effective?” and “What can be done to make science teaching more inclusive?” As a result, it can be clearly seen that JRST has special attention on inclusive approach in science education which should be designed to include traditionally underrepresented groups.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.059 | 0.420 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".