I found Knight’s comparison table of data gathering methods very helpful in thinking about my research project. I had no idea there were so many ways to gather information from the people you’re studying, although all of the methods listed made intuitive sense to me (although of course the more quantitative-sounding ones seemed a bit more artificial). I think that for my research proposal it is fairly clear what methods I will need to use, as most of them don’t suit the situation or would be far too time consuming. I also found the discussion on sample size interesting, because I had always wondered what really passed as “representative” and “legitimate”. I think most people with a higher education will have some sense of what representative samples are, but once you get in to things like ethnography and so on these things become less clear. What is “valid” and “representative”, really? What makes a study matter? Is it the fact that you can, maybe, extrapolate the findings to a larger population, and if you can’t do that do the study’s findings not “count” even though something very interesting might have been uncovered about the group studied? These are all questions I’ll be dealing with as I think about how I would like to go about studying my particular group for my research project proposal.
My favourite part about Luker so far has been the Figuring Out What You Think You Know section within chapter 7. That exercise that she mentions is priceless. Really. I feel like even though I’d already done quite a bit of work on my research proposal, the idea of writing a memo to someone (I imagined it was to one of my past professors) helped me tie up so many loose ends that I was having difficulty stringing together. It also brought up new loose ends, to my initial dismay, but then allowed for an outlet by which I could address them, so that now I don’t feel like I have to worry about it all as much. I know she was talking about issues larger than just a proposal, but it helped all the same for our final assignment.
On a different note, I just want to say that though Knight’s work has been helpful in a pragmatic sense, Luker’s exercises are what really spoke to me throughout this process and helped me not only develop my initial interest but, most importantly, become pretty gung-ho about it all. So, if you’re out there in cyberspace, thanks Luker!
So how did I get to be such a theory-junkie while hating theory as I do? I needed can openers. Here I was with great data, and I needed to know how the smart people in my field thought about how the world was organized, and whether that map could do me any good in understanding my own data. I only like theory when I can connect it to real-world problems that interest me.
– Luker (page 143; emphasis mine)
This observation of Luker’s really stood out to me, particularly in light of ongoing debates about the balance of theory and practice in our programs. Luker admits that she doesn’t find theory particularly interesting in and of itself, even as a career academic, but rather that its value for her is that it can be used as a tool. She uses the metaphor of a can opener, suggesting that theory can help researchers understand real-world problems. I wonder if the broader metaphor of the toolbox might be even more appropriate – if theory cannot only help us understand problems, but solve them as well. (I also wonder whether it might be possible to take this metaphor a little bit further – perhaps knowing theory but not how to apply it is like knowing how to wave tools around, but not how to use them.)
On a very basic level, Wikipedia is an online information infrastructure serving a dual functionality as an information resource from a user perspective (i.e., a collection of linked articles and images that can be consulted, referenced, and used for individual information needs), and as a collaborative interface where one can contribute information anonymously, or create a user account and join an online community. Wikipedia can be analyzed from a technical level as a repository of articles, discussion pages, coding and tags, user accounts, rules and guidelines, all with history pages of their evolution, time-stamped with each addition made. As Star (1999) suggests, one can examine the “hidden mechanisms subtending those processes […] digging to unearth the dramas inherent in system design creating, to restore narrative to what appears to be dead lists” (p. 377). As such, studying information infrastructure is a pursuit that attempts to uncover “embedded strangeness, a second-order one, that of the forgotten, the background, the frozen in place” (Star, 1999, p. 370).
A technique I found useful for analysing Wikipedia’s infrastructure is Star’s (1999) dimension of “transparency” in which she states “infrastructure is transparent to use, in the sense that it does not have to be reinvented each time or assembled for each task, but invisibly supports those tasks” (p.381). As an online information resource, Wikipedia is consistent and straightforward in terms of its navigation from a user perspective as one can access it like any other website. However, in order to actively contribute content, one must be aware of all the policies and guidelines delineated to do so if one endeavours to have their edit last. In addition, one also requires basic knowledge of HTML and how to edit and format text from a technological standpoint. These different approaches to and uses of Wikipedia are transparent insofar as they are clear to anyone who seeks them out; however, whether they are intelligible and by whom is another story. The degree of transparency becomes clearer the more one becomes familiar with the infrastructure, thus leading to another one of Star’s (1999) dimensions: “learned as part of membership” (p.381).
While Star (1999) describes a property of “learned as part of membership” (p.381) to be how “strangers and outsiders encounter infrastructure as a target object to be learned about, [while] [n]ew participants acquire a naturalized familiarity with its objects, as they become members” (p.381), in Wikipedia, the more one immerses oneself within the collaborative community of knowledge production (i.e., [re]presentation), the more one becomes aware of the power structures embedded within it that might not be as observable or even of consequence to the average user.
Another of Star’s (1999) dimensions of infrastructure is how infrastructure is “link[ed] with conventions of practice” (p.381). As Star (1999) explains, “infrastructure both shapes and is shaped by the conventions of a community of practice” (p.381). We can see this dimension manifest in Wikipedia’s infrastructure insofar it, as an information resource, is indebted to the community of collaborative efforts. My efforts engaging with Wikipedia have been unveiling insofar as anonymous contributions I have made have been, more the most part, heavily scrutinized and even deleted; whereas the same edits I have made under my user account have been left unquestioned by otherwise suspect Wikipedians and bots. As such, the communal facet of Wikipedia is not something to be ignored as it is an embodiment of a convention that privileges, or in extreme cases demands, some degree of ownership or accountability to that which is contributed. Therefore, this suggests that perhaps the norm is to question or target, even dismiss, the substantive contributions made anonymously as opposed to focusing efforts on ensuring the best information is made available.
Beyond the elements of infrastructure discussed, I also found Star’s (1999) notion of infrastructure as relational quite useful. The very notion reminded me of dialectic in a Marxist sense, which is a useful tool for appreciating that different aspects of a situation take on a particular meaning depending upon the relationships established between various elements. What was useful about the idea of infrastructure as relational was that it helped me to see more clearly the power relations within the Wikipedia enterprise and the subsequent imbalances between contributors and the arbiters of given contributions. From the point of view of a contributor, my naïve assumptions about what counts as a useful/legitimate contribution were clarified in light of the authority that came to bear and make such decisions.
Grieving.com is a website for people to meet and talk with others who are going through similar experiences. The definition of grief is used broadly, applying to anyone who has lost someone close to them, including causes such as death, the legal system, and divorce, among others. Users are able to express emotions that they may be uncomfortable expressing to those around them, as well as exchange advice on coping mechanisms. This is a particularly interesting case in which to examine emotional identity issues, and there are many questions that can be asked. What is it that draws users to this particular form of support? How often do users frequent the site? Do they consider themselves part of a community? Is this their primary way of dealing with grief, or are they seeking out information or services elsewhere? Methodological challenges could include the ethical decision about whether to study the users overtly or covertly. If studying them overtly, the changes to their risk behaviour must be taken into account. Overall, a study of grieving.com will make an ideal case study for a project on emotional information seeking.
-Melody, Kate and Tracy
I propose that Twitter would serve as an ideal example of an online environment tailored towards emotional information seeking. Twitter is an online micro-blogging and social networking service in which users post short messages of up to 140 characters (“Tweets”). It is tailored to a wide audience (it currently has over 500 million active users) and thus could serve as an excellent subject for any study related to emotional information seeking. User activity consists of posting Tweets, following other Twitter users, and re-posting comments from other Twitter feeds. Because of the condensed nature of the Tweet, many messages consist of brief emotional responses or quips (following the Miami Marlins’ recent fire-sale trade to the Blue Jays, slugger Giancarlo Staunton tweeted: “Alright, I’m pissed off!!! Plain & Simple”). Moreover, many Twitter feeds are devoted to news and political information – Barack Obama’s strong Twitter presence was later judged to be instrumental to his success in the 2008 presidential election. Again, the concise nature of Twitter messages lends itself to brief emotional responses on important and controversial issues. Finally, the fact that Twitter is a social network in addition to a micro-bogging service means that it is an ideal environment in which to study information-seeking behaviour. For these reasons, I believe that Twitter would be an perfect subject for the above-described project.