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Record W4413142673 · doi:10.3138/jsp-2024-0100

Productivity and Publishing Trends of Previously and Newly Established Centrally Funded Universities of India

2025· article· en· W4413142673 on OpenAlexvenueno aff
Rishabh Shrivastava, Puja Paul Srivastava, Bhupinder Singh

Bibliographic record

VenueJournal of Scholarly Publishing · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInnovations in Educational Methods
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityPublishingRegional scienceLibrary scienceGeographyPolitical scienceAgricultural economicsBusinessEconomic geographyEconomic growthEconomicsComputer science

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0140.024
Science and technology studies0.0010.001
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.029
GPT teacher head0.336
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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