Productivity and Publishing Trends of Previously and Newly Established Centrally Funded Universities of India
Bibliographic record
Abstract
The purpose of the present study was to analyse the productivity of the centrally funded universities in India. The previously established central universities (prior to 2009) and newly established central universities (established in 2009 or later) were analysed separately. Data were collected from the Web of Science for the period from 2017 to 2021. It was found that 91.32 % of the publications were journal articles whereas 8.7 % of the publications were review papers. The publications in the study were cited altogether approximately 483,764 times. The highest citations were received by Banaras Hindu University (79,851), while for newly established universities it was Central University of Punjab (9,936). Co-authorship analysis revealed that the median authors ranged from 2 to 6. The data analysis further revealed that Elsevier was the publisher for approximately 47 % of the top 50 cited publications of each university in the study.
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How this classification was reachedexpand
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.010 | 0.093 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".