It’s been nearly four years since The Asthma Files (TAF) really took off (as a collaborative ethnographic project housed on an object-oriented platform). In that time our work has included system design and development, data collection, and lots of project coordination. All of this continues today; we’ve learned that the work of designing and building a digital archive is ongoing. By “we” I mean our “Installation Crew”, a collective of social scientists who have met almost every week for years. We’ve also had scores of students, graduate and undergraduates at a number of institutions, use TAF in their courses, through independent studies, and as a space to think through dissertations. In a highly distributed, long-term, ethnographic project like TAF, we’ve derived a number of modest findings from particular sites and studies; the trick is to make sense of the patterned mosaic emerging over time, which is challenging since the very tools we want to use as a window into our work — data visualization apps leveraging semantic tools, for example — are still being developed.
Given TAF’s structure — thematic filing cabinets where data and projects are organized — we have many small findings, related to specific projects. For example, in our most expansive project “Asthmatic Spaces”, comparisons of data produced by state agencies (health and environmental), have made various layers of knowledge gaps visible, spaces where certain types of data, in certain places, is not available (Frickel, 2009). Knowledge gaps can be produced by an array of factors, both within organizations and because of limited support for cross agency collaboration. Another focus of “Asthmatic Spaces” (which aims to compare the asthma epidemic in a half dozen cities in the U.S. and beyond) is to examine how asthma and air quality data are synced up (or not) and made usable across public, private, and nonprofit organizations.
In another project area, “Asthma Knowledges”, we’ve gained a better understanding of how researchers conceptualize asthma as a complex condition, and how this conceptualization has shifted over the last decade, based on emerging epigenetic research. In “Asthma Care” we’ve learned that many excellent asthma education programs have been developed and studied, yet only a fraction of these programs have been successfully implemented, such as in school settings. Our recent focus has been to figure out what factors are at play when programs are successful.
Below I offer three overarching observations, taken from what our “breakout teams” have learned working on various projects over the last few years:
*In the world of asthma research, data production is uneven in myriad ways. This is the case at multiple levels — seen in public health surveillance and our ability to track asthma nationally, as well as at the state and county level; as seen through big data, generated by epigenetic research; in the scale of air quality monitoring, which is conducted at the level of cities and zip codes rather than at neighborhood or street level. Uneven and fragmented data production is to be expected; as ethnographers, we’re interested in what this unevenness and fragmentation tells us about local infrastructure, environmental policy, and the state of health research. Statistics on asthma prevalence, hospitalizations, and medical visits are easy to come by in New York State and California, for example; experts on these data sets are readily found. In Texas and Tennessee, on the other hand, this kind of information is harder to come by; more work is involved in piecing together data narratives and finding people who can speak to the state of asthma locally. Given that most of what we know about asthma comes from studies conducted in major cities, where large, university-anchored medical systems help organize health infrastructure, we wonder what isn’t being learned about asthma and air quality in smaller cities, rural areas, and the suburbs; what does environmental health (and asthma specifically) look like beyond urban ecologies and communities? We find this particularly interesting given the centrality that place has for asthma as a disease condition and epidemic.
*Asthma research is incredibly diffuse and diverse. Part of the idea for The Asthma Files came from Kim Fortun and Mike Fortun’s work on a previous project where they perceived communication gaps between scientists who might otherwise collaborate (on asthma research). Thus, one of our project goals has been to document and characterize contemporary asthma studies, tracing connections made across research centers and disciplines. In the case of a complex and varied disease like asthma — a condition that looks slightly different from one person to the next and is likely produced by a wide composite of factors — the field of research is exponential, with studies that range from pharmaceutical effects and genetic shifts, to demographic groups, comorbidities, and environmental factors like air pollution, pesticides, and allergens. Admittedly, we’ve been slow to map out different research trajectories and clusters while we work to develop better visualization tools in PECE (see Erik Bigras’s February post on TAF’s platform).
What has been clear in our research, however, is that EPA and/or NIEHS-funded centers undertaking transdisciplinary environmental health research seem to advance collaboration and translation better than smaller scale studies. This suggests that government support is greatly needed in efforts to advance understanding of environmental health problems. Transdisciplinary research centers have the capacity to conduct studies with more participants, over longer periods of time, with more data points. Columbia University’s Center for Children’s Environmental Health provides a great example. Engaging scientists from a range of fields, CCCEH’s birth cohort study has tracked more than 700 mother-child pairs from two New York neighborhoods, collecting data on environmental exposures, child health and development. The Center’s most recent findings suggest that air pollution primes children for a cockroach allergy, which is a determinant of childhood asthma. CCCEH’s work has made substantial contributions to understandings of the complexity of environmental health, as seen in the above findings. Of course, these transdisciplinary centers, which require huge grants, are just one node in the larger field of asthma research. What we know from reviewing this larger field is that 1) most of what we know about asthma is based on studies conducted in major cities, 2) that studies on pharmaceuticals greatly outnumber studies on respiratory therapy; that studies on children outnumber studies on adults; that studies on women outnumber studies on men; and that many of the studies focused on how asthma is shaped by race and ethnicity focus on socioeconomic factors and structural violence; finally, 3) that over the last fifty years, advancements in inhaler technology mechanics and design has been limited in key ways, especially when compared to a broader field of medical devices.
*Given the contextual dimensions of environmental health, responses to asthma are shaped by local factors. What’s been most interesting in our collaborative work is to see what comes from comparing projects, programs, and infrastructure across different sites. What communities and organizations enact what kinds of programs to address the asthma epidemic? What resources and structures are needed to make environmental health work happen? Environmental health research of the scale conducted by CCCEH depends on a number of factors and resources — an available study population, institutional resources, an air monitoring network, and medical infrastructure, not to mention an award winning grassroots organization, WE-ACT for Environmental Justice. Infrastructure can be just as uneven and fragmented as the data collected, and the two are often linked: Despite countless studies that associate air pollution and asthma, less than half of all U.S. counties have monitors to track criteria pollutants. And although asthma education programs have been designed and studied for more than two decades now, implementation is uneven, even in the case of the American Lung Association’s long-standing Open Airways for Schools. This is not to say that asthma information and care isn’t standardized; many improvements have been made to standardize diagnosis and treatment in the last decade. Rather, it’s often the form that care takes that varies from place to place. One example of what has been a successful program is the Asthma and Allergy Foundation of America’s Breathmobile program. Piloted in California more than a decade ago, Breathmobiles serve hundreds of California schools each year and more than 5,000 kids. Not only are eleven Breathmobiles in operation in California, but the program has also been replicated in Phoenix, Baltimore, and Mobile, AL. Part of the program’s success in California can be attributed to the work of the state’s AAFA chapter, and partnerships with health organizations, like the University of Southern California and various medical centers. Importantly, California has historically been a leader in responses to environmental health problem.
As we continue our research, in various fieldsites, grow our archive, and implement new data visualization tools, we hope to expand on these findings and further synthesize from our collective work. And beyond what we’re learning about the asthma epidemic and environmental health in the U.S., we’ve also taken many lessons from our collaborative work, and the platform that organizes us.
February 5th, 2013, by Erik Bigras Comments Off
The Asthma Files is a collaborative ethnographic project focused on the diverse ways people in settings around the world have experienced and responded to the global asthma epidemic and air pollution crisis. It is experimental in a number of ways: It is designed to support collaboration among ethnographers working at different sites, with different foci, such that many particular projects can nest within the larger project structure. This is enabled through a digital platform that we have named PECE: Platform for Experimental, Collaborative Ethnography. PECE is open source and will become shareable with other research groups once we work out its kinks.
PECE has been built to support collaborative, multi-sited, scale-crossing ethnographic research addressing the complex conditions that characterize late industrialism – conditions such as the global asthma epidemic and air pollution crisis; conditions that implicate many different types of actors, locales and systems – social, cultural, political-economic, ecological and technical, calling call for new kinds of ethnographic analyses and collaboration. The platform links researchers in new ways, and activates their engagement with public problems and diverse audiences. The goal is to allow platform users to toggle between jeweller’s eye and systems-level perspective, connecting the dots to see “the big picture” and alternative future pathways.
The Asthma Files has taken us “beyond academia” in a number of ways. Ethnographically, we are engaging an array of professionals, organizations and communities, trying to understand how they have made sense of environmental public health problems. We want to document their sense-making processes, and what has shaped them; we also want to facilitate their sense-making processes – through ethnography that help them understand their own habits of thought and language, and those of others with whom they likely need to work cooperatively. For example, we’ve recently been contacted by a New Orleans housing contractor who wished to know the kind of research being done on asthma and housing in Louisiana. PECE is designed to support this, making space for different kinds of participants at different points in the ethnographic process.
We’ve also gone “beyond academia” to learn how to think about and build a digital platform to support ethnographic work. One step involved selection of the best – for our purposes, for now – online content management system. Quickly, it became apparent that most technical professionals had strong preferences, sometimes based on assessments of functionality, sometimes – it seemed – as a matter of habit. Through a long, comparative process, we ultimately decided on Plone, an open source content management system known for its security capabilities (important in creating space where groups of ethnographers can work together with material, perhaps IRB restricted, out of sight even though online), for its capacity to archive original content (such as interview recordings), and for the ways it supports our effort to nest multiple projects within a larger project structure.
Another important step, which we are still figuring out, is to hire the ongoing technical help we need for PECE. We need ongoing technical help because the platform isn’t finished, as we now envision it. But also because we want the platform to continually evolve as we continue to figure out what kinds of functionality we need to support collaborative ethnographic work. And this may be specific to each project housed on PECE. So we need on-going, ever learning relationships with people who can provide the technical support PECE requires, such as computer scientists, IT specialists, or programmers. As ethnographers, we know that technical professionals will think very differently about the work that we do. And we need to learn to work with this. We need to engage with skills and knowledges that are traditionally outside of the discipline of anthropology by taking on, in a practical way, the continual anthropological challenge of figuring out how difference works.
The Asthma Files and PECE are experiments that have taken us in many new directions – beyond academia, as well as back to basic questions about what should be considered ethnographic material, where theory is in ethnography, how ethnographic findings are best presented, etc. We keep open a call for new collaborators. Let us know if you would like to be in our mix.
Ethnographic Analytics for Anthropological Studies: Adding Value to Ethnography Through IT-based Methods
Ethnographic analytics? What’s that? In short, ethnographic analytics takes advantage of today’s technology to benefit anthropological studies, and is a great example of how science and technology can come together to help us understand and explain much about society and our human condition overall. I suggest that, using the computing power of software tools and techniques, it is possible to construct a set of useful indicators or analytics to complement the five human senses for ethnographic investigation.
Where did the idea of ethnographic analytics originate? How have ethnographic analytics been used and with what results? How can you incorporate them in your work? These are all questions I will address in the following short example of a recent study application in which ethnography and IT-based analytics complemented one another to produce insights about organizational innovation. In this blog, I will focus on one indicator that I have found very useful: an emotion indicator called the Positivity Index.
Over the past three decades, I believe it has been readily apparent that computing has entered our daily lives and especially the business world in the physical forms of desktops, laptops, tablets and smartphones. These devices are tied together with an invisible infrastructure powered by the internet, and now the “cloud,” using software applications to help us do our work, connect with others around the world, and manage many of our daily activities. Two of my colleagues, Julia Gluesing, an anthropologist and also my wife, along with Jim Danowski, a communication professor, and I thought that this new extensive information technology infrastructure could be tapped as resource to help study the diffusion of innovations in globally networked corporations. The result of our collaboration was a five year National Science Foundation (NSF) grant titled: “Accelerating the Diffusion of Innovations: A Digital Diffusion Dashboard Methodology for Global Networked Organizations” (NSF 2010). This mixed methods study provided a very real demonstration of how IT-based methods can complement and extend conventional ethnographic methods. For more detail about the study see the chapter “Being There: the Power of Technology-based Methods” in the new book Advancing Ethnography in Corporate Environments: Challenges and Emerging Opportunities, edited by Brigitte Jordan, which was recently released in 2012.
Overall, we used three software tools, Linguistic Inquiry and Word Count (LIWC), WORDij and Condor, to create a set of seven diffusion indicators or analytics that provided us guidance in selecting a sample of workers and managers for ethnographic interviews and shadowing to explore the context of engineering sub-teams who were working to deliver an innovation for a new vehicle. Working with our sponsors, the company’s legal team, and two university IRBs, we were able to collect 45,000 emails exchanged by a global innovation team working in the early stages of an automotive product innovation. With that data one of the indicators we computed was a weekly “emotion” analytic, which we called the Positivity Index, for the engineering sub-teams using the Linguistic Inquiry and Word Count software (LIWC).
Specifically, we divided the LIWC category percent “posemo” by the category percent ”negemo” to compute the Positivity Index analytic. The “posemo” category contains 407 word or word stems like: “benefit, cool, excit*, great, opportun* etc. The “negemo” category contains 499 word or word stems like: awful, damag*, miss, lose, risk* etc. For example, at the beginning of one project an electrical sub-team had a high Positivity Index about an idea they had using the words “excited potential”, “significant benefit” etc. However, after a few weeks of email exchanges with the transmission group, the Positivity Index plummeted when the combined team realized they would “miss” their deadline, and “risk” not meeting their cost targets. A listing of the LIWC 64 standard categories is available here. Research by Marcial Losada (1999) indicates that a 2.9:1 (positive to negative) ratio is needed for a healthy social system. This is referred to as the “Losada Line.”
If the positivity ratio is above 2.9:1, individuals and business teams flourish, and if it is below 2.9:1, they languish (Fredrickson and Losada 2005). High-performance teams have a positive ratio of 5.6:1 and low-performance teams ratio of 0.4:1. Moreover, there appears to be an upper limit of 11.6:1 where it is possible to have too high a positivity ratio, creating the likelihood that the team will flounder because it does not consider or ignores negative input.
We used the Positive Index to create a graph of scores over time to provide us with an initial sense of each sub-team’s progress in forming and working as a team. Some sub-teams had quite an emotional roller coaster, while the emotion in others did not oscillate nearly as much.
The graphs provided us with a handy, easily understood analytic to explore with the teams to gain a deeper understanding of the context surrounding their work. In another case, rather than email, we used meeting minutes to assess sub-team performance using emotion and found that a Positivity Index derived from minutes also provided a reliable indicator of the health of a team. Over the years, I have calculated the Positivity Index on interviews, newspaper reviews of products, letters, web sites and host of other texts. I have consistently found that it gave me an initial assessment to guide subsequent ethnographic interviews and observations. I have had some false negative readings on occasion, however. In one instance, the texts of plant safety reports described that there were “no deaths or fatalities”. In this case, the Positivity Index gave an inaccurate negative reading of the emotion; “no deaths or fatalities” for the monthly report actually was quite positive. Also, sometimes there is not enough text to generate a percent “negemo,” or negative emotion, to compute a ratio. These outliers have been few, and I now routinely calculate the Positivity Index on my textual research data.
You can try out the Positivity Index using the LIWC software for free. Note: the LIWC website does use an older engine and users will get a slight difference in the results between the Tryonline and the full LIWC version.
The web page will ask you to identify your gender and age, then paste in your text. The words in the text will be counted and a percentage calculated for 7 of the 64 LIWC categories, including self-references (“I,” “me,” “my”), social words, positive emotions, negative emotions, overall cognitive words, articles (“a,” “an,” “the”), and big words (more than six letters). LIWC does provide you with the ability to customize the dictionary with your own vocabulary as well.
My research experience has made me a fan of text analytics that can augment and enhance ethnographic methods with speed and accuracy using the natural language of participants in a very systematic manner. And best of all, the analytics, like the Positivity Index to measure the emotional content of text, are reusable and repeatable.
Fredrickson, Barbara L., and Marcial F. Losada
2005 Positive Affect and the Complex Dynamics of Human Flourishing. American Psychologist 60(7):678–686.
1999 The Complex Dynamics of High Performance Teams. Mathematical and Computer Modeling 30(9–10):179–192.
National Science Foundation (NSF)
2010 Award Abstract No. 0527487. DHB: Accelerating the Diffusion of Innovations: A Digital Diffusion Dashboard Methodology for Global Networked Organizations. Available at http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0527487. Accessed January 15, 2013.
2012 Being There: The Power of Technology-based Methods. In Advancing Ethnography in Corporate Environments: Challenges and Emerging Opportunities. Brigitte Jordan, ed. pp. 38-55. Walnut Creek: Left Coast Press, Inc.
October 26th, 2012, by Patricia G. Lange Comments Off
Anthropology and science and technology studies have moved way beyond academic settings to tackle a range of issues, problems, and policies that are affecting people globally. This section provides a space to talk about conducting projects, research, and applied activities that impact the people whom we partner with to propose solutions to contemporary problems. We encourage contributions from practitioners, consultants, and researchers to talk about their experiences and exchange ideas that emerge from a variety of non-academic settings. Consider this a launching pad for promoting new ideas and connecting with others who are share similar interests and a passion for promoting social justice.