Document (#39319)

Author

McTavish, J.

Title

Everyday life classification practices and technologies : applying domain-analysis to lay understandings of food, health, and eating

Source

Journal of documentation. 71(2015) no.5, S.957-975

Year

2015

Abstract

Purpose - Through the application of domain-analytic principles, the purpose of this paper is to explore how participants' understandings of healthy eating are related to their grouping and classification of foods. Design/methodology/approach - In total, 30 food-interested people were asked to (1) sort a series of 56 statements about food, health, and eating on a scale from "most disagree" to "most agree"; (2)complete an open card sort of 50 foods; and (3) classify these 50 foods on a scale from "most unhealthy" to "most healthy". Exercises (1) and (3) involved Q-methodology, which groups people who share similar understandings of a phenomenon. Findings - Participants' understandings of healthy eating - revealed by the first Q-methodology exercise - were related to shared food priorities, values, and beliefs; these understandings were indirectly connected with food identities, which was not expected. This suggests that lay domain knowledge is difficult to capture and must involve other methodologies than those currently employed in domain-analytic research. Research limitations/implications - Although a small sample of food-interested people were recruited, the purpose of this study was not to make generalized claims about perspectives on healthy eating, but to explore how domain knowledge is related to everyday organizational processes. Originality/value - To "classify" in Library and Information Science (LIS) usually involves an engagement with formally established classification systems. In this paper the author suggests an alternative path for LIS scholars: the investigation of everyday life classification practices. Such an approach has value beyond the idiosyncratic, as the author discusses how these practices can inform LIS researchers' strategies for augmenting the messages provided by static classification technologies.

Content

Vgl.: http://www.emeraldinsight.com/doi/abs/10.1108/JD-08-2014-0105.

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