See You Later, Thick Data – Part 5

This blogpost is part of the methodological series “See You Later, Thick Data – How we experimented with doing collaborative fieldwork as part of an interdisciplinary research project”. In this series, we, a group of anthropologically trained junior scholars, discuss some of the opportunities and challenges we faced when collecting ethnographic data in a week-long, interdisciplinary case study of the Danish democratic festival “The People’s Meeting”. We took on a somewhat different approach to the classic anthropological fieldwork, and in this series, we share our experiences with a highly preplanned, systematic, and collaborative way of collecting ethnographic data that is integrable with other data types.

From “Thick” to “Broad” data?

So far, we haven’t dwelled too much on the cons of the way we approached ethnographic data collection in our case study of the political festival The People’s Meeting. But surely, as our ethnographic focus was prescribed by observation guides and each note typed in a semi-fixed template, there are caveats to consider. Like in more traditional quantitative approaches that we know from the natural sciences, we adopted a much more rigorous mindset than we were used to. With our toolbox of observation guides, the Ethno-platform, and other self-developed schemes, our data collection was preplanned in detail. Before entering the field, we had pinned down exactly what to observe, when to observe it, and how to take note of it. The downside to this approach was the little room left for each of us to pursue new paths or clues unfolding before our eyes. Paths which could not necessarily be predicted in our preplanning. Our approach to the field was far less explorative and flexible than traditional ethnographic fieldwork, putting our project at risk of missing themes which could be central to the analysis of attention flows at the festival. We consequently found that the price of systematization and rigorousness in this case became a tradeoff where we – to some extent – had to say goodbye to elaborate, thick descriptions from the field in favor of a comparable and computationally processable kind of ethnographic data.

Broadband Ethnography

While the data we collected clearly diverged from the more traditional thick ethnographic descriptions, we strived to obtain ethnographic insights which could contribute further than with context to the project. Instead, we collected what we term “broad” data which held compatibility with other data types as well as stand-alone quality. The broad character of our data comes from the three Cs; Compiling, Comparing, and Computational processing, described in the previous posts. Importantly, these qualities implied that the ethnographic data we collected would be compatible with other sorts of data collected at the People’s Meeting by team members on the project.

In telecommunication, broadband means fast transmission of multiple signals at a range of different frequencies. In the same way, we like to think of broad ethnography, or should we say broadband ethnography, as an approach that aims at collecting data which can easily be connected to a wide range of data types from different disciplines. We experienced that broad ethnography was highly useful in an interdisciplinary setting. The data we gathered were carefully filtered and collected with a clear analytical focus. The different empirical material seemed slimmed down on its own, but in combination, our data offered a broad coverage of attention dynamics at the People’s Meeting.

Utilizing Broad Data

An example of where the broadness of our data could come in handy was in combination with ticket sales data and Twitter data related to events at the festival. From the ticket sales data, we could get a sense of which events attract the attention of audiences prior to the festival, and then we could extract information from attention schemes and fieldnotes written during the same events to get a sense of the temporality of aspects of attention flows surrounding particular events. As for the Twitter data, it could be used to examine how political attention surrounding the festival also flows online. We found that stakeholders often tweeted about the issues raised at events at different times during the festival. Some tweets had more interactions in terms of retweets and comments. We were able use data from the Ethno-platform to examine whether certain issues received attention at the same time on the physical festival site as on Twitter by cross-referencing timestamps of tweets and fieldnotes. And by finding the corresponding attention schemes, we could also get a sense of the audience’s attention during the relevant events. In this way, we were able to zoom in and out of our ethnographic data while combining it with other data types. This meant that we could shed light on the attention dynamics at a political festival from several different angles at once.

Picture 9. Ethnographer in the field

Anthropological Ingenuity

Beforehand, we were not used to thinking of ethnographic data as something that could be compatible with other data types to the extent that we intended in this project, and we found ourselves on shaky ground when we started experimenting with computational processing of our broad data. The computational approach to analysis causes a risk of feeling loss of control as it involves handing over some important choices to the machine. Throughout the project, we strived to reach a balance where computational processing of fieldnotes and a structured approach to data collection could help align the data and contribute with analytical insights while keeping the anthropologist in the driver’s seat. Afterall, the depth of the anthropologist’s insight to a given field is one of the discipline’s finest strengths, and during our experiment, we found it useful to keep some flexibility left to qualitatively go through fieldnotes.

Moreover, we found that it was an advantage for us that the ethnographers were anthropologically trained as it took an experienced ethnographic eye to capture the most important dynamics in between the more quantitative observations. Afterall, the devil lies in the detail, and we believe that the nuances captured through the ethnographic observations were critical when observing attention flows at the People’s Meeting as part of an interdisciplinary project.

Conclusion

In this series, we have introduced how we have used a “broad” ethnographic methodology to a case study of attention at the political festival The People’s Meeting. In the beginning of the series, we stated that our approach would result in the collection of broad data due to the qualities of the three Cs which entailed that the data can be: Compiled, Compared, and Computationally processed. The data needed to be compatible with other, more quantitative data sources as we were part of the interdisciplinary project, DISTRACT. There are certainly discoveries that remain in the dark when approaching a field site with this sort of rigorous methodology, but it was a trade-off we were willing to accommodate in this specific study. We acknowledge that this approach isn’t suitable for all enquiries, and we certainly don’t wish to root out traditional ethnographic fieldwork in which we have great faith. We temporarily waved goodbye to the qualities of thick data and dug into the possibilities that broad ethnography might offer.