A Bibliometric Analysis of Educational Studies About “Museum Education
Bibliographic record
Abstract
This study aimed at analyzing the scientific publications about museum education with regard to bibliometric indicators. The study was carried out as a case study, one of the qualitative research methods. The bibliometric data were taken from the WoS database produced by Clarivate Analytics. An online scanning was performed in WoS database. The scan interval involved the dates between 1975 and April 4, 2020. 359 studies related to the museum education were detected in this scan. It was determined that out of these records, 148 of them (%41,22) were included in education/educational research category. The analyses revealed that the type of publications which was encountered mostly were academic articles with 148 studies. In addition to this, it was found that 109 articles were published in the last five years. This rate exhibits that the educational research about the museum education has gained acceleration in recent years. It was detected in the analyses that a total of 470 different key words were used in 148 articles. Moreover, the analyses revealed that the most effective journal was “Journal of Museum Education”. It was determined by the analyses that the researchers from 25 different countries published articles that made contributions to the field. Within this context, it was found that the most active country was the USA and it was followed by Italy, Canada and England. Turkey is ranked 6 out of 25 countries with 7 publications and this shows that serious contributions are made in this field.
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.175 | 0.199 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".