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Tracking and predicting growth areas in science.
1Thomson Scientific, 3501 Market, Philadelphia, PA, 19104, USA.
Scientometrics
|March 28, 2020
Summary
This study uses co-citation clusters to track emerging research fields and predict their growth. Cluster currency, a measure of paper age, predicts future changes in research area size and impact.
Area of Science:
- Bibliometrics
- Scientometrics
- Information Science
Background:
- Understanding the dynamics of scientific fields is crucial for research assessment and funding.
- Co-citation analysis is a bibliometric technique used to identify relationships between documents and research areas.
- Tracking the evolution of research fields requires robust methodologies to capture emerging trends.
Purpose of the Study:
- To investigate the utility of co-citation clusters across time periods for monitoring research area emergence and growth.
- To develop and apply methods for predicting near-term changes in research fields.
- To introduce and validate a new metric, 'in-group citation', to address issues with 'single-issue clusters'.
Main Methods:
- Utilized co-citation clustering on three overlapping six-year data sets (1996-2001, 1997-2002, 1998-2003).
- Reviewed co-citation clustering, mapping, and string formation methodologies.
- Defined and applied 'cluster currency' (average age of highly cited papers) and 'in-group citation' metrics.
Main Results:
- Found a significant association between cluster currency in a prior period and subsequent changes in cluster size and citation frequency.
- Demonstrated that 'cluster currency' can serve as a predictor for research area dynamics.
- Showcased the effectiveness of 'in-group citation' in refining the analysis of 'single-issue clusters'.
Conclusions:
- Co-citation cluster analysis over time effectively tracks the emergence and evolution of research areas.
- Cluster currency is a valuable indicator for predicting the near-term trajectory of scientific fields.
- The 'in-group citation' metric enhances the accuracy of bibliometric analysis by mitigating the impact of specialized, narrow research clusters.
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