1. health and fitness

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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.
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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
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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
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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.