Predicting the importance of current papers.

Conference · Sat Jan 01 04:00:00 EST 2005

OSTI ID:947809

Klavans, Richard ^[1]; Boyack, Kevin W

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1. SciTech Strategies, Inc., Berwyn, PA

This article examines how well one can predict the importance of a current paper (a paper that is recently published in the literature). We look at three factors--journal importance, reference importance and author reputation. Citation-based measures of importance are used for all variables. We find that journal importance is the best predictor (explaining 22.3% out of a potential 29.1% of the variance in the data), and that this correlation value varies significantly by discipline. Journal importance is a better predictor of citation in Computer Science than in any other discipline. While the finding supports the present policy of using journal impact statistics as a surrogate for the importance of current papers, it calls into question the present policy of equally weighting current documents in text-based analyses. We suggest that future researchers take into account the expected importance of a document when attempting to describe the cognitive structure of a field.

• Research Organization: Sandia National Laboratories
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 947809
• Report Number(s): SAND2005-0678C
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
COMPARATIVE EVALUATIONS
DOCUMENT TYPES
FORECASTING
INFORMATION DISSEMINATION