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Descriptor and citation retrieval in the Medical Behavioral Sciences literature: Retrieval overlaps and novelty distribution

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  • Katherine W. McCain

Abstract

Search results for nine topics in the Medical Behavioral Sciences are reanalyzed to compare the overall performance of descriptor and citation search strategies in identifying relevant and novel documents. Overlap percentages between an aggregate “descriptor–based” database (MEDLINE, EXCERPTA MEDICA, PSYCINFO) and an aggregate “citation–based” database (SCISEARCH, SOCIAL SCISEARCH) ranged from 1% to 26%, with a median overlap of 8% relevant retrievals found using both search strategies. For seven topics in which both descriptor and citation strategies produced reasonably substantial retrievals, two patterns of search performance and novelty distribution were observed: 1) Where descriptor and citation retrieval showed little overlap, novelty retrieval percentages differed by 17–23% between the two strategies; 2) Topics with a relatively high percentage retrieval overlap showed little difference (1–4%) in descriptor and citation novelty retrieval percentages. These results reflect the varying partial congruence of two literature networks and represent two different types of subject relevance. © 1989 John Wiley & Sons, Inc.

Suggested Citation

  • Katherine W. McCain, 1989. "Descriptor and citation retrieval in the Medical Behavioral Sciences literature: Retrieval overlaps and novelty distribution," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(2), pages 110-114, March.
    • Handle: RePEc:bla:jamest:v:40:y:1989:i:2:p:110-114
    DOI: 10.1002/(SICI)1097-4571(198903)40:23.0.CO;2-T
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    Cited by:

    1. Yan Yan & Shanwu Tian & Jingjing Zhang, 2020. "The impact of a paper’s new combinations and new components on its citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 895-913, February.
    2. Jianhua Hou & Xiucai Yang & Yang Zhang, 2023. "The effect of social media knowledge cascade: an analysis of scientific papers diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5169-5195, September.
    3. Mikkel Christoffersen, 2004. "Identifying core documents with a multiple evidence relevance filter," Scientometrics, Springer;Akadémiai Kiadó, vol. 61(3), pages 385-394, November.
    4. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.

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