A Bibliometric Analysis on Informal Education Research Developments
Bibliographic record
Abstract
This study analyses the trend in informal and family education research from 2013-2023. The study aims to explore: (1) the development of the number of international publications derived from the Scopus database in 2013-2023; (2) core journals in international publications; (3) researchers’ productivity; (4) the development of publications based on the subject/field; (5) the development of research publications based on keywords (Co-word) and based on authors (Co-author). The data were collected by browsing through Scopus using the following keywords; informal, education, and family. The search explores categories, article titles, abstracts, and keywords within the time frame of 2013-2023. The data of the number of publications per year, authors, and subjects were analysed using Microsoft Excel 2019. Meanwhile, the development of publications was analysed using the VosViewer application. The results showed that (1) the highest publication was found in 2022, with 197 publications (16.58%); (2) PLOS One Journal was the most productive publication (19 publications); (3) the institution with the miNost affiliations was the University of Toronto with 23 publications; (4) the United States (US) contributed the most with 397 publications; (5) the most productive author was Zimmerman, H.T. (10 publications); (6) the most frequent subject was social science (546 publications); (7) the research development in this field is categorized into 8 clusters based on co-word analysis
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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.163 | 0.248 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".