A Scientometric Study of Quality Assessment and Higher Education
Bibliographic record
Abstract
This study evaluates the research publication of Quality Assessment and Higher Education for the period of 2015-2025. The purpose of this study is to analyse the research outcome on QA & HE using scientometrics tools and techniques such as Annual research output by the researcher, kinds of documents, top ten authors, affiliation wise, country wise distribution papers, journal wise and Language wise on QA & HE. The study revealed that the highest number of research papers was published in the year 2025 with 1270(14.49%). The top three journals based on the number of publications were BMC Medical Education with 206 (12.05%), Plos one with 163(9.54%) and Sustainability with 137(8.02%). The majority of publications were articles with 7015 (80.02%) chosen by the researcher. Zhang, Y. with 30 (13.51%) publications and share the 1st place. The top affiliation is the University of Toronto, Canada with 197 (12.20%) publications. The most productive country was The United States of America (2653) publications. “Quality” is the most frequent word with 1563 occurrences from 2015 to 2025. This study will be helpful for further research in the field of scentomentrics, library professionals who are working in higher education and quality assessment.
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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 | 0.031 | 0.167 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.046 | 0.111 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".