Digital Democracy Remixed

I recently transitioned from my study of the many reasons why the voice of DC taxi drivers is largely absent from online discussions into a study of the powerful voice of the Kenyan people in shaping their political narrative using social media. I discovered a few interesting things about digital democracy and social media research along the way, and the contrast between the groups was particularly useful.

Here are some key points:

  • The methods of sensemaking that journalists use in social media is similar to other methods of social media research, except for a few key factors, the most important of which is that the bar for verification is higher
  • The search for identifiable news sources is important to journalists and stands in contrast with research methods that are built on anonymity. This means that the input that journalists will ultimately use will be on a smaller scale than the automated analyses of large datasets widely used in social media research.
  • The ultimate information sources for journalists will be small, but the phenomena that will capture their attention will likely be big. Although journalists need to dig deep into information, something in the large expanse of social media conversation must capture or flag their initial attention
  • It takes some social media savvy to catch the attention of journalists. This social media savvy outweighs linguistic correctness in the ultimate process of getting noticed. Journalists act as intermediaries between social media participants and a larger public audience, and part of the intermediary process is language correcting.
  • Social media savvy is not just about being online. It is about participating in social media platforms in a publicly accessible way in regards to publicly relevant topics and using the patterned dialogic conventions of the platform on a scale that can ultimately draw attention. Many people and publics go online but do not do this.

The analysis of social media data for this project was particularly interesting. My data source was the comments following this posting on the Al Jazeera English Facebook feed.

fb

It evolved quite organically. After a number of rounds of coding I noticed that I kept drawing diagrams in the margins of some of the comments. I combined the diagrams into this framework:

scales

Once this framework was built, I looked closely at the ways in which participants used this framework. Sometimes participants made distinct discursive moves between these levels. But when I tried to map the participants’ movements on their individual diagrams, I noticed that my depictions of their movements rarely matched when I returned to a diagram. Although my coding of the framework was very reliable, my coding of the movements was not at all. This led me to notice that oftentimes the frames were being used more indexically. Participants were indexing levels of the frame, and this indexical process created powerful frame shifts. So, on the level of Kenyan politics exclusively, Uhuru’s crimes had one meaning. But juxtaposed against the crimes of other national leaders’ Uhuru’s crimes had a dramatically different meaning. Similarly, when the legitimacy of the ICC was questioned, the charges took on a dramatically different meaning. When Uhuru’s crimes were embedded in the postcolonial East vs West dynamic, they shrunk to the degree that the indictments seemed petty and hypocritical. And, ultimately, when religion was invoked the persecution of one man seemed wholly irrelevant and sacrilegious.

These powerful frame shifts enable the Kenyan public to have a powerful, narrative changing voice in social media. And their social media savvy enables them to gain the attention of media sources that amplify their voices and thus redefine their public narrative.

readyforcnn

Still grappling with demographics

Last year I wrote about my changing perspective on demographic variables. My grappling has continued since then.
I think of it as an academic puberty of sorts.

I remember the many crazy thought exercises I subjected myself to as a teenager, as I tried to forge my own set of beliefs and my own place in the world. I questioned everything. At times I was under so much construction that it was a wonder I functioned at all. Thankfully, I survived to enter my twenties intact. But lately I have been caught in a similar thought exercise of sorts, second guessing the use of sociological demographic variables in research.

Two sample projects mark two sides of the argument. One is a potential study of the climate for underrepresented faculty members in physics departments. In our exploration of this subject, the meaning of underrepresented was raised. Indeed there are a number of ways in which a faculty member could be underrepresented or made uncomfortable: gender, race, ethnicity, accent, bodily differences or disabilities, sexual orientation, religion, … At some point, one could ask whether it matters which of these inspired prejudicial or different treatment, or whether the hostile climate is, in and of itself, important to note. Does it make sense to tick off which of a set of possible prejudices are stronger or weaker at a particular department? Or does it matter first that the uncomfortable climate exists, and that personal differences that should be professionally irrelevant are coming into professional play. One could argue that the climate should be the first phase of the study, and any demographics could be secondary. One might be particularly tempted to argue for this arrangement given the small sizes of the departments and hesitation among many faculty members to supply information that could identify them personally.

If that was the only project on my mind, I might be tempted to take a more deconstructionist view of demographic variables altogether. But there is another project that I’m working on that argues against the deconstructionist view- the Global Survey of Physicists.

(Side or backstory: The global survey is kind of a pet project of mine, and it was the project that led me to grad school. Working on it involved coordinating survey design, translation and dissemination with representatives from over 100 countries. This was our first translation project. It began in English and was then translated into 7 additional languages. The translation process took almost a full year and was full of unexpected complications. Near the end of this phase, I attended a talk at the Bureau of Labor Statistics by Yuling Pan from Census. The talk was entitled ‘the Sociolinguistics of Survey Translation.’ I attended it never having heard of Sociolinguistics before. During the course of the talk, Yuling detailed and dissected experiences that paralleled my own into useful pieces and diagnosed and described some of the challenges I had encountered in detail. I was so impressed with her talk that I googled Sociolinguistics as soon as I returned to my office, discovered the MLC a few minutes later. One month later I was visiting Georgetown and working on my application for the MLC. I like to say it was like being swept up off my feet and then engaging in a happy shotgun marriage)

The Global Survey was designed to elicit gender differences in terms of experiences, climate, resources and opportunities, as well as the effects of personal and family constraints and decisions on school and career. The survey worked particularly well, and each dive into the data proves fascinating. This week I delved deeper into the dynamics of one country and saw women’s sources of support erode as they progressed further into school and work, saw the women transition from a virtual parity in school to difficult careers, beginning with their significantly larger chance of having to choose their job because it was the only offer they received, and becoming significantly worse with the introduction of kids. In fact, we found through this survey that kids tend to slow women’s careers and accelerate men’s!

What do these findings say about the use of demographic variables? They certainly validate their usefulness and cause me to wonder whether a lack of focus on demographics would lessen the usefulness of the faculty study. Here I’m reminded that it is important, when discussing demographic variables, to keep in mind that they are not arbitrary. They reflect ways of seeing that are deeply engrained in society. Gender, for example, is the first thing to note about a baby, and it determines a great deal from that point in. Excluding race or ethnicity seems foolish, too, in a society that so deeply engrains these distinctions.

The problem may be in the a priori or unconsidered applications of demographic variables. All too often, the same tired set of variables are dredged up without first considering whether they would even provide a useful distinction or the most useful cuts to a dataset. A recent example of this is the study that garnered some press about racial differences in e-learning. From what I read of the study, all e-learning was collapsed into a single entity, an outcome or dependent variable (as in some kind if measure of success of e-learning), and run by a set of traditional x’s or independent variables, like race and socioeconomic status. In this case, I would have preferred to first see a deeper look into the mechanics of e-learning than a knee jerk rush to the demographic variables. What kind of e-learning course was it? What kinds of interaction were fostered between the students and the teacher, material and other students? So many experiences of e-learning were collapsed together, and differences in course types and learning environments make for more useful and actionable recommendations than demographics ever could.

In the case of the faculty and global surveys as well, one should ask what approaches to the data would yield the most useful analyses. Finding demographic differences leads to what- an awareness of discrimination? Discrimination is deep seeded and not easily cured. It is easy to document and difficult to fix. And yet, more specific information about climate, resources and opportunities could be more useful or actionable. It helps to ask what we can achieve through our research. Are we simply validating or proving known societal differences or are we working to create actionable recommendations? What are the most useful distinctions?

Most likely, if you take the time to carefully consider the information you collect, the usefulness of your analyses and the validity of your hypotheses, you are one step above anyone rotely applying demographic variables out of ill-considered habit. Kudos to you for that!

Dispatch from the quantitative | qualitative border

On Tuesday evening I attended my first WAPA meeting (Washington Association of Professional Anthropologists). This group meets monthly, first with a happy hour and then with a speaker. Because I have more of a quantitative background, the work of professional anthropologists really blows my mind. The topics are wide ranging and the work interesting and innovative. I’ve been sorry to miss so many of their gatherings.

This week’s topic was near and dear to my heart in two ways.

1. The work was done in a survey context as a qualitative investigation preceding the development of survey questions. As a professional survey methodologist, I have worked through the surprisingly complicated question writing process many hundreds of times, so this approach really fascinates me!

2. The work surrounded the topic of childbirth. As a mother of two and a [partially] trained birth assistant, I love to talk about childbirth.

The purpose of the study at hand was to explore infant mortality in greater depth by investigating certain aspects of the delivery process. The topics of interest included:

– whether the birth was attended by a professional or not
– whether the birth was at home or in a medical facility
– delivery of the placenta
– how soon after the birth the baby was wiped
– cord cutting and tying
– whether the baby was swaddled and whether the baby’s head was covered
– how soon the baby was bathed

The study was based on 80 respondents (half facility births, half homebirths) (half moms of newborns, half moms of 1-2 year olds) from each of two countries. The researchers collected two kinds of data: extensive unstructured interviews and survey questions. The interviews were coded using Atlas ti into specific, identifiable, repeated events that were relevant to infant mortality and then placed onto a timeline. The timeline guided the recommended order of the survey questions.

One audience member shared that she would have collected stories of “what is a normal childbirth?” from participants in addition to the women’s personal stories. Her focus with this tactic was to collect the language with which people usually discuss these events in childbirth. She mentioned that her field was linguistic anthropology. The language she was talking about is referred to by survey researchers as “native terms-” essentially the terms that people normally use when discussing a given topic. One of the goals of question writing is to write a question using the terms that a respondent would naturally use to classify their response, making the response process easier for the respondent and collecting higher quality data. The presenters mentioned that, although they did not collect normative stories, collecting native terms was a part of their research process and recommendations.

The topics of focus are problematic ones to investigate. Most women can tell whether or not they gave birth in a facility and whether or not the birth was attended by a professional. Women can usually remember their labor and delivery in detail (usually for the rest of their lives), as well as the first time they held and fed their babies. Often women can also remember the delivery of the placenta or whether or not they hemorrhaged or tore significantly during the birth process.

But other aspects of the birth, such as the cord cutting and tying and the first wiping and swaddling of the baby, are usually done by someone other than the mother (if there is someone else present). They often don’t command the attention of the mother, who is full of emotion and adrenaline and catching her breath from an all encompassing, life changingly powerful experience. These moments are often not as memorable as others, and the mothers are often not as fully aware of them or able to report them.

I wondered if the moms were able to use the same level of detail in retelling these parts of their stories? Was there any indication that these sections of the stories they told were their own personal stories and not a general recounting of events as they are supposed to happen? In survey research, we talk about satisficing, or providing an answer because an answer is expected, not because it is correct. In societies where babies are frequently born at home, people often grow up around childbirth and know the general, expected order of events. How would the results of the study have been different if the researchers had used a slightly different approach: instead of assuming that the mothers would be able to recount all of these details of their own experiences, the researchers could have taken a deeper look at who performed the target activities, how detailed an account of the activities the mothers were able to provide, and the nature of the mom’s involvement or role in the target activities.

I wondered if working with this alternative approach would have led to questions more like “The next few questions refer to the moments after your baby was born and the first time you held and nursed your baby. Was the baby already wiped when you first held and nursed them? Was the babies cord already cut and tied? Was the baby already swaddled? Was the baby’s head already covered?” Although questions like these wouldn’t separate out the first 5 minutes from the first 10, they would likely be easier for the mom to answer and yield more complete and accurate responses.

All in all, this event was a fantastic one. I learned about an area of research that I hadn’t known existed. The speaker was great, and the audience was engaged. If you have an opportunity to attend a WAPA event, I highly recommend it.

Turns out Ethnography happens one slice at a time

Some of you may have noticed that I promised to report some research and then didn’t.

Last semester, for my Ethnography of Communication class, I did an Ethnography of DC taxi drivers. The theme of the Ethnography was “the voice of the drivers.” It was multilayered, and it involved data from a great variety of sources. I had hoped to share my final paper for the class here, but that won’t work for three reasons.

1.) The nature of Ethnography. Ethnography involves collecting a great deal of data and then choosing what to report, in what way, and in what context. The goal of the final paper was to reflect on the methodology. This was an important exercise, but I really wanted to share more of my findings and less of my methodology here.

2.) The particular aspect of my findings that I most want to share here has to do with online discourse. Specifically, I want to examine the lack of representation of the drivers perspective online. There are quite a few different ways to accomplish this. I have tried to do it a number of ways, using different slices of data and using different analytic strategies. But I haven’t decided which is the best set of data or method of analysis. But I am a very lucky researcher. Next week I’m headed to a workshop at Radbound University in Nijmegen, Netherlands. The workshop is on the Microanalysis of Online Discourse. I am eager to bring my data and methodological questions and to recieve insight from such an amazing array of researchers. I am also very eager to see what they bring!

Much of the discussion in the analysis of online discourse either excludes the issue of representation altogether or focuses on it entirely. Social media is often hailed as the great democratizer of communication. Internet access was long seen as the biggest obstacle to this new democracy . From this starting point, much of the research has evolved to consider more of nuances of differential use, including the complicated nature of internet access as well as behavior and goals of internet users. This part of my findings is an example of differential use and of different styles of participation. Working with this data has changed the way I see social media and the way I understand the democratization of news.

3.) Scope. The other major reason why I haven’t shared my findings is because of the sheer scope of this project. I was fortunate enough to only have taken one class last semester, which left me the freedom to work much harder on it. Also, as a working/student mom, I chose a project that involved my whole family in an auto-ethnographic way, so much of my work brought me closer to my family, rather than farther apart (spending time away from family to study is one of the hardest parts of working student motherhood!)

I have amassed quite a bit of data at this point, and I plan to write a few different papers using it.

Stay tuned, because I will release slices of it. But have some patience, because each slice will only be released in its own good time.

 

At this point, I feel the need to reference the Hutzler Banana Slicer

Turns out, Ethnography is more like this:

 

than like this:

Data Storytelling

In the beginning of our Ethnography of Communication class, one of the students asked about the kinds of papers one writes about an ethnography. It seemed like a simple question at the time. In order to report on ethnographic data, the researcher chooses a theme and then pulls out the parts of their data that fit the theme. Now that I’m at the point in my ethnography where I’m choosing what to report, I can safely say that this question is not one with an easy answer.

At this point, I’ve gathered together a tremendous amount of data about DC taxi drivers. I’ve already given my final presentation for my class, and written most of my final paper. But the data gathering phase hasn’t ended yet. I have been wondering whether I have enough data gathered together to write a book, and I probably could write a book, but that still doesn’t make my project feel complete. I don’t feel like the window I’ve carved is large enough to do this topic any justice.

The story that I set out to tell about the drivers is one of their absence in the online public sphere. As the wife of a DC driver, I was sick and tired of seeing blog posts and newspaper articles with seemingly unending streams of offensive, ignorant, or simply one sided comments. This story turns out to be one with many layers, one that goes far beyond issues of internet access, delves deeply into matters of differential use of technology, and one that strikes fractures into the soil of the grand potential of participatory democracy. It is also a story grounded in countless daily interactions, involving a large number of participants and situations. The question is large, the data abundant, and the paths to the story many. Each more narrow path begs a depth that is hungry for more data and more analysis. Each answer is defined by more questions. More specifically, do I start with the rides? With a specific ride? With the drivers? With a specific driver? With a specific piece of legislation? With one online discussion or theme? How can I make sure that my analysis is grounded and objective? How far do I trace the story, and which parts of the story does it leave out? What happens with the rest of the story? What is my responsibility and to whom?

This paper will clearly not be the capstone to the ethnography, just one story told through the data I’ve gathered together in the past few months. More stories can be told, and will be told with the data. Specifically, I’m hoping to delve more deeply into the driver’s social networks, for their role in information exchange. And the fallout from stylistic differences in online discussions. And, more prescriptively, into ways that drivers voices can be better represented in the public sphere. And maybe more?

It feels strange to write a paper that isn’t descriptive of the data as a whole. Every other project that I’ve worked on has led to a single publication that summarized the whole set. It seems strange, coming from a quantitative perspective where the data strongly confines the limits of what can and cannot be said in the report and what is more or less important to include in the report, to have a choice of data, and, more importantly, a choice of story to tell. Instead of pages of numbers to look through, compare and describe, I’m entering the final week of this project with the same cloud of ambiguity that has lingered throughout. And I’m looking for ways that my data can determine what can and cannot be reported on and what stories should be told. Where, in this sea of data, is my life raft of objectivity? (Hear that note of drama? That comes from the lack of sleep and heightened anxiety that finals bring about- one part of formal education that I will not miss!!)

I have promised to share my paper here once it has been written. I might end up making some changes before sharing it, but I will definitely share it. My biggest hope is that it will inspire some fresh, better informed conversation on the taxi situation in DC and on what it means to be represented in a participatory democracy.

“Not everything that can be counted counts”

“Not everything that counts can be counted, and not everything that can be counted counts” – sign in Einstein’s Princeton office

This quote is from one of my favorite survey reminder postcards of all time, along with an image from from the Emilio Segre visual archives. The postcard layout was an easy and pleasant decision made in association with a straightforward survey we have conducted for nearly a quarter century. …If only social media analysis could be so easy, pleasant or straightforward!

I am in the process of conducting an ethnography of DC taxi drivers. I was motivated to do this study because of the persistent disconnect between the experiences and reports of the taxi drivers and riders I hear from regularly and the snarky (I know this term does not seem technical, but it is absolutely data motivated!) riders who dominate participatory media sources online. My goal at this point of the project is to chase down the disconnect in media participation and see how it maps to policy deliberations and offline experiences. This week I decided to explore ways of quantifying the disconnect.

Inspired by this article in jedem (the eJournal of eDemocracy and Open Government), I decided to start my search using framework based in Social Network Analysis (SNA), in order to use elements of connectedness, authority and relevance as a base. Fortunately, SNA frameworks are widely available to analysts on a budget in the form of web search engines! I went through the first 22 search results for a particular area of interest to my study: the mandatory GPS policy. Of these 22 sites, only 11 had active web 2.0 components. Across all of these sites, there were just two comments from drivers. Three of the sites that didn’t have any comments from drivers did have one post each that sympathized with or defended DC taxi drivers. The remaining three sites had no responses from taxi drivers and no sympathetic responses in defense of the drivers. Barring a couple of comments that were difficult to divine, the rest of the comments were negative comments about DC taxi drivers or the DC taxi industry. This matched my expectations, and, predictably, didn’t match any of my interviews or offline investigations.

The question at this point is one of denominator.

The easiest denominator to use, and, in fact, the least complicated was the number of sites. Using this denominator, only one quarter of the sites had any representation from a DC taxi driver. This is significant, given that the discussions were about aspects of their livelihood, and the drivers will be the most closely affected by the regulatory changes. This is a good, solid statistic from which to investigate the influence of web 2.0 on local policy enactment. However, it doesn’t begin to show the lack of representation the way that a denominator such as number of posts, number of posters, or number of opinions would have. But each one of these alternative denominators has its own set of headaches. Does it matter if one poster expresses an opinion once and another expresses another, slightly different opinion more than once? If everyone agrees, what should the denominator be? What about responses that contain links that are now defunct or insider references that aren’t meaningful to me? Should I consider measures of social capital, endorsements, social connectedness, or the backgrounds of individual posters?

The simplest figure also doesn’t show one of the most striking aspects of this finding; the relative markedness of these posts. In the context of predominantly short, snarky and clever responses, one of the comments began with a formal “Dear DC city councilmembers and intelligent  taxpayers,” and the other spread over three dense, winding posts in large paragraph form.

This brings up an important aspect of social media; that of social action. If every comment is a social action with social intentions, what are the intentions of the posters and how can these be identified? I don’t believe that the majority of posts left were intended as a voice in local politics, but the comments from the drivers clearly were. The majority of posts represent attempts to warrant social capital using humor, not attempts to have a voice in local politics. And they repeated phrases that are often repeated in web 2.0 discussions about the DC taxi situation, but rarely repeated elsewhere. This observation, of course, is pretty meaningless without being anchored to the data itself, both quantitatively and qualitatively. And it makes for some interesting ‘next steps’ in a project that is certainly not short of ‘next steps.’

The main point I want to make here is about the nature of variables in social media research. Compared to a survey, where you ask a question, determined in advance, and have a set of answers to work with in your analysis, you are free to choose your own variables for your analysis. Each choice brings with it a set of constraints and advantages, and some fit your data better than others. But the path to analysis can be a more difficult path to take, and more justification about the choices you make is important. To augment this, a quantitative analysis, which can sometimes have very arbitrary or less clear choices included in it, is best supplemented with a qualitative analysis that delves into the answers themselves and why they fit the coding structure you have imposed.

In all of this, I have quite a bit of work out ahead of me.

I conducted my first diversity training today…

One of the perks of my grad program is learning how to conduct diversity training.

Today I was able to put that skill to use for the first time. I conducted a workshop for a local parents group about Talking with your Kids about Race and Diversity. I co-facilitated it with Elvira Magomedova, a recent graduate from the MLC program who has more experience and more of a focus in this area. It was a really interesting and rewarding experience.

We did 4 activities:

1. We introduced ourselves by telling our immigration stories. I saw this last week at an open house at my daughter’s middle school, and it profoundly reminded me about the personal ways in which we all embody global history and the immigrant nature of the US. Between feuding clans in Ireland,  narrow escapes from the holocaust and traveling singers in Europe, this exercise is both powerful and fun. Characters and events really come alive, and everyone is left on a more equal footing.

2. For the 2nd activity, we explored the ways in which we identify ourselves. We each put a circle in the center of a sheet of paper, an then we added four bubble spokes with groups or cultures or ways in which we identify ourselves. The exercise came from Cultural Awareness Learning Module One. At the bottom of the page, we explored these relationships more deeply, e.g. “I’m a parent, but I’m not a stay at home parent” or “I’m Muslim, but I’m not practicing my religion.” We spoke in depth about our pages in pairs and then shared some with the group.

3. This is a fun activity for parents and kid alike. We split into two groups, culture A and culture B. Each culture has a list of practices, e.g. standing close or far, making eye contact or not, extensive vs minimal greetings or leavetaking, shaking or not shaking hands, … The groups learn, practice, and then mingle. This is a profoundly awkward activity!

After mingling, we get back into the group and discuss the experience. It soon becomes obvious that people take differences in “culture” personally. People complain that it seemed like their interlocuters were just trying to get away from them, or seemed overly interested in them, or…. They also complain about how hard it is to adjust your practices to act in the prescribed way.

This exercise is a good way for people to understand the ways in which conflicting cultural norms play out, and it helps parents to understand how to work out misunderstandings with their kids.

4. Finally, my daughter made a slide show of people from all over the world. The people varied in countless physical ways from each other, and we used them to stimulate conversation about physical differences. As adults, we tend to ascribe a bevvy of sociological baggage to these physical differences, but the reality is that, unless we’re Steven Colbert, there are striking physical differences between people. As parents, we are often taken aback when our kids speak openly about differences that we’ve grown accustomed to not talking about. It’s natural and normal to wonder how to handle these observations.

The upshot of this conversation is that describing anyone by a single physical category doesn’t really make sense. If you’re talking about a physical description of someone, you have a number of physical features to comment on. Whereas referring to anyone by a single physical feature could be offensive, a more detailed description is simply a more accurate physical description. We don’t have to use judgmental words, like “good hair,” but that shouldn’t stop us from talking about curly, straight, wavy, thick or thin. We can talk about people in terms of their height or body shape, face shape, hair texture, color or style, eye shape or color, mouth shape, ear size, nose style, skin tone, and so much more. Artificial racial or ethnic groupings don’t *really* describe what someone looks like, talks like, or has experienced.

More than this, once we have seen people in any kind of action, we have their actions and our relationship with them to use as resources. Given all of those resources, choosing race or ethnicity as a first descriptive level with our kids, or even using that descriptor and stopping, sends the message to the kids that that is the only feature that matters. It draws boundaries before it begins conversations. It passes “us and them” along.

Race and ethnicity are one way to describe a person, but they are far from the only way. And they, more than any other way, carry the most baggage. Does that mean they should be avoided or declared taboo?

This week in my Ethnography of Communication class, we each went to Gallaudet, the deaf university in DC, and observed. One of my classmates commented about her discomfort with her lack of fluency in ASL, or American Sign Language. Her comment reminded me of my kids and their cousins. My kids speak English, and only a little bit of Amharic and Tigrinya. Some of their cousins only spoke Tigrinya when they met. Some only spoke Swedish. Some spoke English with very different accents. But the language barriers never stopped them from playing with each other.

In fact, we talk about teaching our kids about diversity, but our kids should be the ones to teach us!

Here are the main lessons I’ve learned from my kids:

1. Don’t cut yourself off from people because you don’t share a common language. Communication actually runs much deeper than language. I think, for example, of one of my sisters inlaw. When we first met, we didn’t have a common language. But the more I was able to get to know her over time, the more we share. I really cherish my relationship with her, and I wouldn’t have it if I had let my language concerns get in the way of communicating with her.

2. People vary a lot, strikingly, in physical ways. These are worthy of comment, okay to notice, and important parts of what make people unique.

3. If you cut yourself off from discomfort or potential differences, you draw a line between you and many of the people around you.

4. It is okay to be wrong, or to still be learning. Learning is a lifelong process. Just because we’re adults doesn’t mean we have to have it all down pat. Don’t be afraid to fail, to mess up. Your fear will get you nowhere. How could you have learned anything if you were afraid of messing up?

In sum, this experience was a powerful one and an interesting one. I sincerely hope that the conversations we began will continue.

* Edited to Add:

Thandie Newton TED talk, Embracing Otherness

Chimamanda Adichie TED talk: The danger of a single story

GREAT letter with loads of resources: http://goodmenproject.com/ethics-values/why-i-dont-want-to-talk-about-race/

an interesting article that we read in class: why white parents don’t talk about race

another interesting article: Lippi Green 1997 Teaching Children How to Discriminate

 

The Bones of Solid Research?

What are the elements that make research “research” and not just “observation?” Where are the bones of the beast, and do all strategies share the same skeleton?

Last Thursday, in my Ethnography of Communication class, we spent the first half hour of class time taking field notes in the library coffee shop. Two parts of the experience struck me the hardest.

1.) I was exhausted. Class came at the end of a long, full work day, toward the end of a week that was full of back to school nights, work, homework and board meetings. I began my observation by ordering a (badly needed) coffee. My goal as I ordered was to see how few words I had to utter in order to complete the transaction. (In my defense, I am usually relatively talkative and friendly…) The experience of observing and speaking as little as possible reminded me of one of the coolest things I’d come across in my degree study: Charlotte Linde, SocioRocketScientist at NASA

2.) Charlotte Linde, SocioRocketScientist at NASA. Dr Linde had come to speak with the GU Linguistics department early in my tenure as a grad student. She mentioned that her thesis had been about the geography of communication- specifically: How did the layout of an (her?) apartment building help shape communication within it?

This idea had struck me, and stayed with me, but it didn’t really make sense until I began to study Ethnography of Communication. In the coffee shop, I structured my fieldnotes like a map and investigated it in terms of zones of activities. Then I investigated expectations and conventions of communication in each zone. As a follow-up to this activity, I’ll either return to the same shop or head to another coffee shop to do some contrastive mapping.

The process of Ethnography embodies the dynamic between quantitative and qualitative methods for me. When I read ethnographic research, I really find myself obsessing over ‘what makes this research?’ and ‘how is each statement justified?’ Survey methodology, which I am still doing every day at work, is so deeply structured that less structured research is, by contrast, a bit bewildering or shocking. Reading about qualitative methodology makes it seem so much more dependable and structured than reading ethnographic research papers does.

Much of the process of learning ethnography is learning yourself; your priorities, your organization, … learning why you notice what you do and evaluate it the way you do… Conversely, much of the process of reading ethnographic research seems to involve evaluation or skepticism of the researcher, the researcher’s perspective and the researcher’s interpretation. As a reader, the places where the researcher’s perspective varies from mine is clear and easy to see, as much as my own perspective is invisible to me.

All of this leads me back to the big questions I’m grappling with. Is this structured observational method the basis for all research? And how much structure does observation need to have in order to qualify as research?

I’d be interested to hear what you think of these issues!

Unlocking patterns in language

In linguistics study, we quickly learn that all language is patterned. Although the actual words we produce vary widely, the process of production does not. The process of constructing baby talk was found to be consistent across kids from 15 different languages. When any two people who do not speak overlapping languages come together and try to speak, the process is the same. When we look at any large body of data, we quickly learn that just about any linguistic phenomena is subject to statistical likelihood. Grammatical patterns govern the basic structure of what we see in the corpus. Variations in language use may tweak these patterns, but each variation is a patterned tweak with its own set of statistical likelihoods. Variations that people are quick to call bastardizations are actually patterned departures from what those people consider to be “standard” english. Understanding “differences not defecits” is a crucially important part of understanding and processing language, because any variation, even texting shorthand, “broken english,” or slang, can be better understood and used once its underlying structure is recognized.

The patterns in language extend beyond grammar to word usage. The most frequent words in a corpus are function words such as “a” and “the,” and the most frequent collocations are combinations like “and the” or “and then it.” These patterns govern the findings of a lot of investigations into textual data. A certain phrase may show up as a frequent member of a dataset simply because it is a common or lexicalized expression, and another combination may not appear because it is more rare- this could be particularly problematic, because what is rare is often more noticeable or important.

Here are some good starter questions to ask to better understand your textual data:

1) Where did this data come from? What was it’s original purpose and context?

2) What did the speakers intend to accomplish by producing this text?

3) What type of data or text, or genre, does this represent?

4) How was this data collected? Where is it from?

5) Who are the speakers? What is their relationship to eachother?

6) Is there any cohesion to the text?

7) What language is the text in? What is the linguistic background of the speakers?

8) Who is the intended audience?

9) What kind of repetition do you see in the text? What about repetition within the context of a conversation? What about repetition of outside elements?

10) What stands out as relatively unusual or rare within the body of text?

11) What is relatively common within the dataset?

12) What register is the text written in? Casual? Academic? Formal? Informal?

13) Pronoun use. Always look at pronoun use. It’s almost always enlightening.

These types of questions will take you much further into your dataset that the knee-jerk question “What is this text about?”

Now, go forth and research! …And be sure to report back!

Rethinking demographics in research

I read a blog post on the LoveStats blog today that referred to one of the most widely regarded critiques of social media research: the lack of demographic information.

In traditional survey research, demographic information is a critically important piece of the analysis. We often ask questions like “Yes 50% of the respondents said they had encountered gender harassment, but what is the breakdown by gender?” The prospect of not having this demographic information is a large enough game changer to cast the field of social media research into the shade.

Here I’d like to take a sidestep and borrow a debate from linguistics. In the linguistic subfield of conversation analysis, there are two main streams of thought about analysis. One believes in gathering as much outside data as possible, often through ethnographic research, to inform a detailed understanding of the conversation. The second stream is rooted in the purity of the data. This stream emphasizes our dynamic construction of identity over the stability of identity. The underlying foundation of this stream is that we continually construct and reconstruct the most important and relevant elements of our identity in the process of our interaction. Take, for example, a study of an interaction between a doctor and a patient. The first school would bring into the analysis a body of knowledge about interactions between doctors and patients. The second would believe that this body of knowledge is potentially irrelevant or even corrupting to the analysis, and if the relationship is in fact relevant it will be constructed within the excerpt of study. This begs the question: are all interactions between doctors and patients primarily doctor patient interactions? We could address this further through the concept of framing and embedded frames (a la Goffman), but we won’t do that right now.

Instead, I’ll ask another question:
If we are studying gender discrimination, is it necessary to have a variable for gender within our datasouce?

My kneejerk reaction to this question, because of my quantitative background, is yes. But looking deeper: is gender always relevant? This does strongly depend on the datasource, so let’s assume for this example that the stimulus was a question on a survey that was not directly about discrimination, but rather more general (e.g. “Additional Comments:”).

What if we took that second CA approach, the purist approach, and say that where gender is applicable to the response it will be constructed within that response. The question now becomes ‘how is gender constructed within a response?’ This is a beautiful and interesting question for a linguist, and it may be a question that much better fits the underlying data and provides deeper insight into the data. It also turns the age old analytic strategy on its head. Now we can ask whether a priori assumptions that the demographics could or do matter are just rote research or truly the productive and informative measures that we’ve built them up to be?

I believe that this is a key difference between analysis types. In the qualitative analysis of open ended survey questions, it isn’t very meaningful to say x% of the respondents mentioned z, and y% of the respondents mentioned d, because a nonmention of z or d is not really meaningful. Instead we go deeper into the data to see what was said about d or z. So the goal is not prevalence, but description. On the other hand, prevalence is a hugely important aspect of quantitative analysis, as are other fun statistics which feed off of demographic variables.

The lesson in all of this is to think carefully about what is meaningful information that is relevant to your analysis and not to make assumptions across analytic strategies.