A Scientometric Study of Quality Assessment and Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it