Status of Contrasting Citations in Funded Research
Published: 2024
Author(s) Name: Sarita Gulati, Babita Yadav, Atasi Sinhababu, Rupak Chakravarty |
Author(s) Affiliation: Shivalik Institute of Education & Research, Mohali, Punjab, India.
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Abstract
This study attempts to analyse the context of the citations with the help of an AI and deep learning model-based web tool, Scite. The study analyses contrasting, supporting, mentioning, Lifetime SI and total cites metrics of citation statements and also examines the relationship between Lifetime SI and total cites, total cites and contrasting; total cites and supporting and contrasting and supporting. The data for this research has been derived from Scite’s Funders citation data and was sorted based on contrasting citation records by the Funding Institutions in descending order for meaningful inferences. A correlation test was applied using the open-source statistical software Jamovi. Based on an analysis of citation statements related to research productivity, it was found that the “National Natural Science Foundation of China” has the highest number of publications in each category of editorial notices. Lifetime SI and total cites exhibit a weak positive correlation. In contrast, the parameters total cites and contrasting, total cites and supporting and contrasting and supporting show a significant and strong positive correlation.
Keywords: Scite, Citation Statements, Artificial Intelligence, Deep Learning, Citations, Machine Learning, Funders/Funding Institutions
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