McTavish. EKO3 2015 1 Permeating Everyday Life Classification
Transcription
McTavish. EKO3 2015 1 Permeating Everyday Life Classification
McTavish. EKO3 2015 1 Permeating Everyday Life Classification Technologies – The Productive Power of Nutritional Classifications Jill McTavish, MLIS, Ph.D., Clinical Librarian, London Health Science Centre, Health Sciences Library, 339 Windermere Road, London, ON, Canada, N6G 2V4 [email protected] Abstract: Background. Everyday life (EDL) classification technologies refer to static, non-neutral tools that order the world. They are “a set of boxes (metaphorical or literal) into which things can be put to then do some kind of work – bureaucratic or knowledge production” (Bowker and Star 1999, 10). An example of a formal but relatively static EDL classification technology is the public health food guide (e.g., ChooseMyPlate.gov), which organizes food items into food groups and according to their healthiness. EDL classification processes refer to the conceptual distinctions people make in their everyday lives (McTavish in press). While these processes are less discussed in library and information science (LIS) due to perceptions of their idiosyncrasies (Mai 2008) and because descriptions of users’ classificatory or searching practices are perceived to be unhelpful for considering how to build bibliographic knowledge organization systems (Hjørland 2013), McTavish (in press) has shown that they can be useful for pointing out the limitations in messages provided by EDL classification technologies and can help to suggest ways to augment these systems. In this paper, I report on the EDL classification practices of registered dietitians (RDs). Rather than revealing the limitations of EDL classification technologies, I discuss how the classification practices of RDs reaffirm the understandings of “health” and organization of food offered by the food guide and their discipline – at times to the detriment of non-standard understandings of health. With a growing interest in EDL classification practices and technologies in LIS, it is important to address the limitations of these technologies and to think about ways to make them permeable to all users. As Bowker and Star (1999, 6) have reminded us, there is a “moral and ethical agenda” involved in querying classification systems – including EDL technologies – as each category decision represents an inescapable, ethical choice to uncover. McTavish. EKO3 2015 2 Methods. In this paper, I report on a selection of findings from a larger study about EDL classification practices. Data collection for this project took place in a mid-sized city in southwestern Ontario. Eighteen RDs were recruited for this study: three public health dietitians were recruited for their specific knowledge about public health priorities and 15 Registered Dietitians were selected randomly from a public register of RDs. RDs were asked to complete an open card sort of 50 foods considered to be “healthy” and “unhealthy” by a governmentproduced food guide. Then they were asked a series of questions about their understanding of healthy eating, how they deal with clients who have non-standard understandings of healthy eating, and the potential ways to modify the food guide based on alternative understandings of health. Results. RDs’ understanding of healthy eating and organization of foods closely matched the organization of food offered by a government-produced food guide. RDs saw the food guide as an important tool to translate expert, evidenced-based, nutritional knowledge to the general public. When they were asked to discuss the organization of foods that were produced by different food-interested communities, such as vegans, their preference for evidence-based information led several of them to emphasize the “misinformation” and “non-credible” sources that their clients rely upon to make their eating decisions. Discussion. Feinberg (2010) suggests that classifications, as documents, can make an argument by using structural evidence, such as which categories are included and how they are arranged and related, as well as resource evidence, such as which resources are selected and how they are assigned to categories in the organizational scheme. The food guide is an example of an EDL classification technology whose organization of foods reinforces certain understandings of health – ones that have been critiqued by health groups, such as Harvard’s School of Public Health (n.d.), as well as researchers investigating different food-interested (McTavish in press) and ethno-cultural (James 2004; Ristovski-Slijepcevic et al. 2008) communities. In this research I show how the EDL classification practices of RDs extends the understanding of health and organization of food offered by the food guide, which poses problems for communities that organize and think about foods differently. Like Olson (2001), I suggest that rather than create new food guides that match the needs of different food-interested communities, we can “develop McTavish. EKO3 2015 3 an ethical relationship with the Other through techniques for making the limits of our existing information systems permeable” (p. 659). In the case of the food guide, RDs offered several suggestions for how to make the food guide more permeable, such as linking the food guide to other credible information sources more suited to specific food-interested communities. While the food guide is not an LIS technology, I will discuss how insights from this study can be applied to increase the permeability of LIS tools, as well as the ways that LIS theories about ethical knowledge organization can be brought to bear on EDL classification technologies in our community. References: Bowker, Geoffrey C., and Susan Leigh Star. 1999. Sorting Things out: Classification and Its Consequences. Inside Technology. Cambridge, Mass: MIT Press. Feinberg, Melanie. 2010. “Two Kinds of Evidence: How Information Systems Form Rhetorical Arguments.” Journal of Documentation 66 (4): 491–512. doi:10.1108/00220411011052920. Harvard T.H. Chan School of Public Health. “Healthy Eating Plate vs. USDA’s MyPlate". (n.d). Accessed April 2, 2015. http://www.hsph.harvard.edu/nutritionsource/healthy-eatingplate-vs-usda-myplate/ Hjørland, Birger. 2013. “User-Based and Cognitive Approaches to Knowledge Organization : A Theoretical Analysis of the Research Literature” 40 (1): 11–27. James, Delores. 2004. “Factors Influencing Food Choices, Dietary Intake, and Nutrition-Related Attitudes among African Americans: Application of a Culturally Sensitive Model.” Ethnicity & Health 9 (4): 349–67. Mai, Jens-Erik. 2008. “Actors, Domains, and Constraints in the Design and Construction of Controlled Vocabularies.” Knowledge Organization 35 (1): 16–21. McTavish, Jill. in press. “Everyday Life Classification Practices and Technologies – Applying Domain-Analysis to Lay Understandings of Food, Health, and Eating.” Journal of Documentation McTavish. EKO3 2015 4 Olson, Hope A. (2001). "The power to name: Representation in library catalogs." Signs 26(3): 639-668. Ristovski-Slijepcevic, Svetlana, Gwen E. Chapman, and Brenda L. Beagan. 2008. “Engaging with Healthy Eating Discourse(s): Ways of Knowing about Food and Health in Three Ethnocultural Groups in Canada.” Appetite 50 (1): 167–78.