方法对比

	Information Encountering Analysis	Berrypicking Evaluation
领域	Library Information Science	Library Information Science
方法族	Process / pipeline	Process / pipeline
起源年份	1999	1989
提出者	Sanda Erdelez	Marcia J. Bates
类型	Conceptual framework and analysis of accidental information discovery	Model and evaluative lens for evolving, non-linear online search
开创性文献	Erdelez, S. (1999). Information encountering: It's more than just bumping into information. Bulletin of the American Society for Information Science, 25(3), 26-29.	Bates, M. J. (1989). The design of browsing and berrypicking techniques for the online search interface. Online Review, 13(5), 407-424.
别名	Information Encountering, Erdelez Information Encountering, Accidental Information Discovery, Incidental Information Acquisition Analysis	Berrypicking Model, Evolving Search Model, Bates Berrypicking, Berry-Picking Search
相关	3	3
摘要	Information Encountering Analysis, developed by Sanda Erdelez and articulated in her 1999 Bulletin of the American Society for Information Science article 'Information encountering: It's more than just bumping into information,' studies how people acquire useful information by accident — while searching for something else, or while not searching at all. Against the dominant picture of information behaviour as goal-directed seeking, Erdelez foregrounds serendipitous, incidental discovery as a distinct and important mode. She models an encounter as a sequence of functional steps — noticing, stopping, examining, capturing and returning — and classifies people by how readily they encounter information, from non-encounterers to 'super-encounterers' who experience and exploit accidental discovery frequently. The framework gives a vocabulary and analytic structure for a phenomenon long dismissed as mere luck.	Marcia Bates's berrypicking model, introduced in her 1989 Online Review article 'The design of browsing and berrypicking techniques for the online search interface,' rejects the classic picture of information retrieval as a single query matched against a database to return one optimal set. Real searches, Bates argued, are evolving: the query shifts as the searcher learns, and useful information is gathered bit-at-a-time, like picking scattered berries, from many different sources using many different techniques. Used as an evaluative lens, the berrypicking model judges search systems and interfaces not by how well they answer one fixed query but by how well they support a continually changing need — letting searchers move fluidly among footnote chasing, citation searching, journal runs, area scans and subject searches as their understanding develops.