A bibliometric analysis of university administration in scientific literature
Bibliographic record
Abstract
Objective: This paper identifies the key characteristics of university administration as an object of study, a subject of analysis, and a focus of research. Design/Methodology/Approach. The study employed a bibliometric methodology that utilized indicators of bibliographic elements. The Scopus platform served as the data source, from which a comprehensive selection of published works on university administration was obtained. Additionally, a thorough bibliographic review was conducted to describe the main identified research fronts. Results/Discussion. University administration, as a subject of study, encompasses a wide range of topics. There is a noticeable lack of specialized bibliographic production and a limited presence of publications in high-impact journals. Recent topics primarily focus on postcoital mental health studies and the role of university administration in this context. Notably, the United States, Canada, and China are at the forefront of research in this field. It is important to emphasize the significant increase in scientific output since the early 2000s. Conclusions: Research in university administration has seen a significant rise in the quantity of scientific publications indexed in Scopus. Originality/Value. The study's originality is manifested in the absence of precedents for similar results in the thematic area under study.
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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 | 0.019 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.204 | 0.274 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".