The bibliometric journey of IJATE from local to global
Bibliographic record
Abstract
International Journal of Assessment Tools in Education (IJATE) is one of the educational journals that is indexed in major worldwide databases such as Web of Science (WoS) and ERIC. This study presents the bibliometric characteristics of articles published in IJATE between 2014 and 2021 through the bibliometric analyses. Harzing's “Publish or Perish software” was used to collect citation data from WoS and Google Scholar databases as a tool to analyze the impact of articles. Firstly, when contributing institutions are analyzed, especially in recent, it is seen that researchers from countries such as France and Kuwait have been contributing to the journal with publications produced through international collaboration. Moreover, when the average citation numbers per article is calculated, it is understood that Australia (13) and Canada (3.5) are the countries that contribute significantly to the visibility of the journal. Such a trend will contribute significantly to the international recognition of the journal soon. On the other hand, there is a statistically significant positive relationship (r=0.339; p<0.01) between usage count and the number of citations by WoS. Our results reveal that while the number of references used in the articles was in consistent with the literature, the average article title lengths (12±3) were slightly longer than the ideal length (10±3). The results will provide important contributions to editors, reviewers, and authors in the journey of IJATE from local to global. The findings can guide authors, the editors and referees and also serve as a potential roadmap for the future studies and journal.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.098 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".