Trends of cultural studies in science education: A systematic review from 1973 to 2023
Bibliographic record
Abstract
In this study, a systematic literature review on cultural studies in science education between 1973 and 2023 was conducted through the Scopus database. Content analysis was used in this study in which 277 articles from the last 50 years were reviewed. According to the guidelines of Petticrew and Roberts (2006), trends of cultural studies in science education were investigated in terms of annual accounts, number of articles by authors, distribution of articles by country, most productive journals, most cited articles, and most preferred research methods. The results showed that research on cultural studies in science education fluctuated between slowing down and positively accelerating. The growth rate of the articles peaked in 2013. Three authors have three papers, twenty authors have two papers, and the rest of author each have one paper in terms of the distribution of number of authors. Analysis revealed that most articles come from countries such as the United States, the United Kingdom, and Germany, while interest in this area is growing in countries such as Australia, Canada, and Turkey. Cultural Studies of Science Education, International Journal of Science Education, International Journal of Psychology, and Science and Education being the most productive journals in this field. The most cited article with 160 citations was published in 2012 by Nagengast and Marsh (2012) in Journal of Educational Psychology. Finally, the most popular research design was quantitative research method followed by mixed (quantitative and qualitative together) research method. Some implications are proposed for future studies.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.045 | 0.046 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".