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A comparison of group and individual performance among subject experts and untrained workers at the document retrieval task

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  • W. John Wilbur

Abstract

Useful retrieval depends on the ability to predict which documents a user will find helpful in answer to a query. Our interest is the common case when no information is provided about the user other than the query and the query is in natural language. In this setting it is well accepted that a human can make useful predictions in the form of judgments about what will likely prove useful to another human. We present data showing that when the predictions of a group of humans are averaged, the result is a better predictor. If performance is measured as a precision, the group performance increases with the size of the group and approaches a limit of approximately 50% improvement over average individual performance on our data. Superior performance by groups raises the question of how. The groups we studied were subject experts and a natural question was whether the superior performance resulted from the pooling of their subject knowledge. In order to answer this question we studied also a group of untrained individuals. To our surprise we found that while untrained individuals had a somewhat inferior performance compared to trained individuals, the group of untrained individuals together performed better than any single trained individual and almost at the level of the trained group. © 1998 John Wiley & Sons, Inc.

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  • W. John Wilbur, 1998. "A comparison of group and individual performance among subject experts and untrained workers at the document retrieval task," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(6), pages 517-529.
  • Handle: RePEc:bla:jamest:v:49:y:1998:i:6:p:517-529
    DOI: 10.1002/(SICI)1097-4571(19980501)49:63.0.CO;2-T
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    Cited by:

    1. Ashraf Labib & Salem Chakhar & Lorraine Hope & John Shimell & Mark Malinowski, 2022. "Analysis of noise and bias errors in intelligence information systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1755-1775, December.

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