Metric Studies in LIS – Bibliometrics, Scientometric, Webometrics, Altmetrics.
Updated: Jun 8
Measuring the Impact of Knowledge: The Evolution of Metric Studies in LIS
The landscape of Library and Information Science (LIS) has undergone a massive transformation over the past century. As the volume of published research grows exponentially, the need to measure, track, and evaluate scientific output has become critical. This evaluation is driven by metric studies—a suite of quantitative disciplines that analyze the creation, dissemination, and impact of knowledge. The chronological evolution of these studies from Bibliometrics and Scientometrics in 1969, to Webometrics in 1997, and finally Altmetrics in 2010, reflects our transition from a print-based society to a hyper-connected digital world.
Bibliometrics: The Foundation of Literature Measurement The formal term "bibliometrics" was first coined by Alan Pritchard in 1969 in his article "Statistical bibliography or bibliometrics". It is defined as the application of mathematical and statistical techniques to books and other media of communication. Before Pritchard, similar concepts were explored; for instance, Dr. S.R. Ranganathan introduced the term "Librametry" in 1948 to apply mathematical and statistical techniques to streamline day-to-day library activities and management.
Bibliometrics serves as the foundational tool for assessing academic output and is anchored by three fundamental laws:
Bradford’s Law of Scattering: Propounded by Samuel Clement Bradford in 1934, this law describes how articles on a specific subject are scattered across different journals, helping libraries identify core journals for collection development.
Lotka’s Law: Formulated by Alfred J. Lotka in 1926, this law measures author productivity, demonstrating that a very small number of highly productive authors contribute the vast majority of literature in any given field.
Zipf’s Law: This law predicts the frequency of words within a text, providing a mathematical framework for understanding language use in documents.
A major breakthrough in bibliometrics was the creation of the Science Citation Index by Eugene Garfield in 1964, which pioneered citation indexing and led to the development of the Journal Impact Factor. The impact factor evaluates a journal's relative importance by reflecting the average number of citations its recent articles receive.
Scientometrics: Analyzing Science as an Economic Activity While bibliometrics focuses on recorded information, Scientometrics takes a broader approach. Coined by Vassily V. Nalimov and Z. M. Mulchenko in 1969, the term originated from the Russian word naukometriya. Scientometrics is defined as the study of the quantitative aspects of science as a discipline or economic activity.
Scientometrics overlaps significantly with bibliometrics but is specifically applied to science policy-making. It evaluates the impact of scientific research, institutional performance, and national scientific output. Through scientometrics, researchers and policymakers can identify centers of excellence, map patterns of collaborative research, and analyze the geographic distribution of scientific discoveries. Advancements in this field also introduced new author-level metrics, such as the h-index, proposed by physicist Jorge E. Hirsch in 2005 to measure both the productivity and the impact of a researcher's scholarly output.
Webometrics: Charting the Digital Frontier As the World Wide Web became a universal repository of human knowledge and culture, traditional print-based metrics required an upgrade. The term "Webometrics" was coined by Tomas C. Almind and Peter Ingwersen in 1997 to address this shift.
According to Lennart Björneborn and Peter Ingwersen (2004), webometrics is the study of the quantitative aspects of the construction and use of information resources, structures, and technologies on the Web, drawing heavily on bibliometric and informetric approaches. Mike Thelwall, a major contributor to the field, identified four key areas of webometric research:
Link Analysis: The quantitative study of hyperlinks between web pages.
Web Citation Analysis: Tracking how academic research is cited across the internet.
Search Engine Evaluation: Analyzing the performance and retrieval capabilities of search engines.
Web Data Analysis: Measuring Web 2.0 applications and user interactions.
Webometrics allows researchers to map the invisible networks of the internet, turning the web's chaotic structure into measurable data.
Altmetrics: The Social Web and Real-Time Impact The most recent evolution in metric studies is "Altmetrics" (alternative metrics), a term coined by Jason Priem in 2010. Traditional bibliometric indicators, like citation counts, accumulate at a very slow rate and often fail to capture the immediate, real-world impact of a publication. Altmetrics emerged to measure the creation and study of new metrics based on the Social Web.
Altmetrics track a wide variety of interactions, including how often an original article is viewed, downloaded, bookmarked, or discussed on academic social networks and mainstream social media. Tools like Plum Analytics gather data to categorize impact into distinct areas: articles that are "saved," "cited," "recommended," or "discussed". Because altmetric results can easily be inflated by social media manipulation, a qualitative backend is necessary to verify the data. Despite this challenge, altmetrics provide vital, real-time insights into how people interact with individual pieces of research output outside of traditional scholarly publishing.
Conclusion The progression from bibliometrics to altmetrics represents a continuous effort by the LIS community to accurately measure the footprint of knowledge. While bibliometrics and scientometrics remain the gold standard for formal academic evaluation, webometrics and altmetrics provide a crucial, expanded view of how information lives, breathes, and is consumed in the modern digital ecosystem.
Would you like to explore the specific formulas behind any of these metrics, such as the h-index or the Journal Impact Factor, next?
Comments