Bibliometrics is a field of study that involves the quantitative analysis of scholarly publications and their citations to evaluate various aspects of research, such as impact, productivity, and collaboration. The history of bibliometrics can be traced back to the pioneering work of Eugene Garfield, who introduced the first citation index for sciences in 1953 (Masic, 2016).

Long before the first science citation index was created there was Shepard's Citations, a legal citation index established in 1873, that provides a comprehensive record of American court cases and judgments and their complete history recorded in a simple code (Smith, 2007). The system classifies citations into categories such as "Distinguished," "Criticized," "Limited," "Questioned," "Overruled," or "Disapproved" (Spriggs, 2000). Shepard’s Citations was a significant influence on Eugene Garfield and the history of bibliometrics.

While the idea of classifying citations into different categories has been discussed for decades, until recently, there has been no large-scale implementation of different types of citations. Consequently, citation analysis, a key component of bibliometrics, treats citations more or less as if they were the same despite intent.

The advent of Smart Citations by scite.ai represents a significant advancement in the field of bibliometrics: the world’s first large-scale citation metric that is based not simply on the existence of a citation, but its content. Scite is a Smart Citation index that utilizes machine learning to retrieve and contextualize scientific research, classifying citations based on their intent and context (Nicholson et al., 2021). This innovative approach addresses the challenge of understanding the true impact and nature of citations, providing researchers with a more comprehensive view of how their work is being cited and referenced within the scholarly literature (Nicholson et al., 2021). The use of machine learning and deep learning techniques in scite represents a paradigm shift in the analysis of citations, offering a more nuanced and insightful understanding of scholarly impact.

Now with the cooperation of dozens of major publishers, smart citations are moving the field of bibliometrics forward. Smart Citations from scite can help publishers display better metrics, assist researchers in evaluating and understanding research better, and ultimately improve the overall quality of research. If you’re interested in using scite to help with your publishing program, your university, or your research organization, book some time here: https://scite.ai/request-a-demo.

References:

Mašić, I. (2016). Index factors for assessing the scientific journal validity, it's articles and their authors. Journal of Anthropology Reports, 01(01). https://doi.org/10.35248/2684-1304.16.1.103

Nicholson, J. M., Mordaunt, M., Lopez, P., Uppala, A., Rosati, D., Rodrigues, N. P., … & Rife, S. C. (2021). Scite: a smart citation index that displays the context of citations and classifies their intent using deep learning. Quantitative Science Studies, 2(3), 882-898. https://doi.org/10.1162/qss_a_00146

Smith, D. R. (2007). Historical development of the journal impact factor and its relevance for occupational health. Industrial Health, 45(6), 730-742. https://doi.org/10.2486/indhealth.45.730

Spriggs, J. F. and Hansford, T. G. (2000). Measuring legal change: the reliability and validity of shepard's citations. Political Research Quarterly, 53(2), 327. https://doi.org/10.2307/449284