Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part Two)

Here is the second part of my interview with Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He describes garbology is and what kind of work he does as a data scientist garbologist.

Here is Part 1, Part 3, and Part 4 of our interview.

Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.

Resources:

Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part One)

I interviewed Gideon Singer, Director of Spacial Data Science at Litterati, for my Interview Series. He discusses his mission to combine garbology, anthropology, and data science to better understand humanity and the trash we leave behind. In this first part, he describes the connections he has found between these various fields.

Here is Part 2, Part 3, and Part 4 of our interview.

Gideon Singer is an applied anthropologist in the business of exploring societies through the waste, litter, rubbish, and other detritus they leave behind. As a self-proclaimed digital garbologist, his work juxtaposes digital ethnography with archaeology and spatial data science.

Resources:

Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 2 of 2)

Here is the second part of three in my conversation with Tanner Greene. He discusses his strategies for transitioning from graduate school to UX research and his recommendations for any fellow student seeking to do the same.

Here is Part 1 of our interview.

Tanner Greene is a UX Researcher and Ph.D. Candidate at the University of Virginia, where he’s finishing a dissertation on the history of vaporwave, a music genre created on social media platforms. Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.

Resources We Referenced:

For more context on my interview series in general, click here.

Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 1 of 2)

For my next installment in my Interview Series, I interviewed Tanner Greene. He recently received his doctorate from the University of Virginia for his research on the digital music genre, vapor wave. He primarily used qualitative means but has also taught himself Python to be able to employ quantitative textual analysis into his project. It is a good example of how to integrate qualitative digital ethnographic techniques with quantitative natural language processing.

In this first part, he discusses why he decided to study the vapor wave community and his experiences learning Python to conduct statistical analysis with.

Here is Part 2 of our interview.

Tanner’s interests straddle math and the humanities, spanning digital cultures, user metadata, and a long-dormant statistics ability he wants to revive. In his spare time, Tanner enjoys writing about music, playing video games, and dreaming about learning SQL.

Resources We Referenced:

For more context on my interview series in general, click here.

Ethno-Data: Introduction to My Blog

            Hello, my name is Stephen Paff. I am a data scientist and an ethnographer. The goal of this blog is to explore the integration of data science and ethnography as an exciting and innovative way to understand people, whether consumers, users, fellow employees, or anyone else.

            I want to think publicly. Ideas worth having develop in conversation, and through this blog, I hope to present my integrative vision so that others can potentially use it to develop their own visions and in turn help shape mine.

Please Note: Because my blog straddles two technical areas, I will split my posts based on how in-depth they go into each technical expertise. Many posts I will write for a general audience. I will write some posts, though, for data scientists discussing technical matters within that field, and other posts will focus on technical topics withn ethnography for anthropologists and other ethnographers. At the top of each post, I will provide the following disclaimers:

Data Science Technical Level: None, Moderate, or Advanced
Ethnography Technical Level: None, Moderate, or Advanced

Integrating Ethnography and Data Science

As a data scientist and ethnographer, I have worked on many types of research projects. In professional and business settings, I am excited by the enormous growth in both data science and ethnography but have been frustrated by how, despite recent developments that make them more similar, their respective teams seem to be growing apart and competitively against each other.

Within academia, quantitative and qualitative research methods have developed historically as distinct and competing approaches as if one has to choose which direction to take when doing research: departments or individual researchers specialize in one or the other and fight over scarce research funding. One major justification for this division has been the perception that quantitative approaches tend to be prescriptive and top-down compared with qualitative approaches which tend to be to descriptive and bottom-up. That many professional research contexts have inherited this division is unfortunate.

Recent developments in data science draw parallels with qualitative research and if anything, could be a starting point for collaborative intermingling. What has developed as “traditional” statistics taught in introductory statistics courses is generally top-down, assuming that data follows a prescribed, ideal model and asking regimented questions based on that ideal model. Within the development of machine learning been a shift towards models uniquely tailored to the data and context in question, developed and refined iteratively.[i] These trends may show signs of breaking down the top-down nature of traditional statistics work.

If there was ever a time to integrate quantitative data science and qualitative ethnographic research, it is now. In the increasingly important “data economy,” understanding users/consumers is vital to developing strategic business practices. In the business world, both socially-oriented data scientists and ethnographers are experts in understanding users/consumers, but separating them into competing groups only prevents true synthesis of their insights. Integrating the two should not just include combining the respective research teams and their projects but also encouraging researchers to develop expertise in both instead of simply specializing in one or the other. New creative energy could burst forth when we no longer treat these as distinct methodologies or specialties.


[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12.

Photo credit #1: Frank V at  https://unsplash.com/photos/IFLgWYlT2fI

Photo credit #2: Arif Wahid at https://unsplash.com/photos/y3FkHW1cyBE

Interview Series: Introduction

woman wearing blue top beside table
Photo by Christina Morillo on Pexels.com

Many people who seek to integrate ethnography and data science have told me that they feel alienated or sidelined: like they are “lone wolves” having to figure out how to combine them without anyone or any resources to help them. Those who are interested in learning how to connect the two, in particular, tell me that they feel stuck without sufficient resources or support to explore connections between them. I do not think either anthropology and qualitative research or data science (two fields I am a part of) do a very good job understanding the other.

Thus, I started this interview series to highlight the experiences of those who in some way integrate ethnography with data science. My intent is to catalog their stories and ideas to help those considering doing similar work. I will interview people from a variety of backgrounds and industries (including anthropologists, data scientists, artists, software engineers, and so on) discussing what is most significant for them in their field.  In general, though, my interviews will focus on the following questions or themes:

    1. What is it like integrating data science and ethnography (or whatever other related activities or methodologies that particular person uses)?

    2. What skills and abilities have you found helpful for such work, and what recommendations do you have for cultivating those skills?

    3. How would you recommend education institutions and employers teach and/or foster these skills?

    4. How has your joint background influenced how you view and approach the world, data science, ethnography, and/or the disciplines or fields you are a part of?

I started conducting these interviews in the fall of 2021. After a brief break, I started what I am calling my “Season 2” of interviews in the fall of 2022 with an ever evolving list of professionals. I am thankful to talk with everyone I have, and I hope you enjoy them as well.

If you would like to be interviewed or know someone else doing this type of work who you would recommend I interview, please reach out to me on my Contact Me page. Feel free to also reach out if you have any particular burning questions that you would like to see me ask.

Season 1 Interviews:Date Posted:
Maker Anthropologist in the Tech Field: Interview with Astrid CounteeSeptember 8, 2021
Data Scientist, Anthropologist, and Entrepreneur: Interview with Schaun WheelerSeptember 21, 2021
Anthropologist in Fintech: Interview with Priyanka Dass SahariaOctober 18, 2021
Data Scientist, Entrepreneur, and Artist: Interview with Emi Harry (Part 1 of 3)November 2, 2021
Data Scientist, Entrepreneur, and Artist: Interview with Emi Harry (Part 2 of 3)November 16, 2021
Data Scientist, Entrepreneur, and Artist: Interview with Emi Harry (Part 3 of 3)November 30, 2021
Anti-Corruption Anthropologist in Kazakhstan: Interview with Olga ShiyanDecember 14, 2021
Digital Anthropology and Artificial Intelligence: Interview with Scarleth HerreraJanuary 25, 2022
Season 2 Interviews:Date Posted:
Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 1 of 3)August 17, 2022
Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 2 of 3)August 31, 2022
Tech Anthropologist Working and Product Manager: Interview with Matt Artz (Part 3 of 3)September 14, 2022
The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 1 of 3)September 28, 2022
The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 2 of 3)October 12, 2022
The Promises and Failures of Current Artificial Intelligence Technology: An Interview with Gemma Clavell at Eticas (Part 3 of 3)October 26, 2022
Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 1)November 9, 2022
Applying Computational Ethnography and Statistics to Vapor Wave: Interview with Tanner Greene (Part 2)November 22, 2022
Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part 1 of 4)December 6, 2022
Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part 2 of 4)December 20, 2022
Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part 3 of 4)January 3, 2023
Trash Data Science: Garbology, Anthropology, and Spatial Data Science – Conversation with Gideon Singer (Part 4 of 4)January 17, 2023
User-Centric Thinking in Data Science: Conversation with Anna Wu at Google Cloud (Part 1 of 3)January 31, 2023
User-Centric Thinking in Data Science: Conversation with Anna Wu at Google Cloud (Part 2 of 3)February 14, 2023
User-Centric Thinking in Data Science: Conversation with Anna Wu at Google Cloud (Part 3 of 3)February 28, 2023
Data Science and Game Design: Conversation with Clayton Sisson (Part 1 of 3)March 14, 2023
Data Science and Game Design: Conversation with Clayton Sisson (Part 2 of 3)March 28, 2023
Data Science and Game Design: Conversation with Clayton Sisson (Part 3 of 3)April 11, 2023
Data Science Storytelling: Quantitative UX Research in Google Cloud with Randy Au (Part 1 of 2)May 9, 2023
Data Science Storytelling: Quantitative UX Research in Google Cloud with Randy Au (Part 2 of 2)May 23, 2023

Rethinking Ethnography in Anthropology

This is a follow-up on my previous article about the difference between anthropology and ethnography. In this article, I discuss recent trends within anthropology to either revitalize ethnography and/or rethink its status as the primary research methodology within the discipline.

We anthropologists should consider expanding beyond the ethnographic toolkit. That could involve redefining what it means to conduct ethnography in such a way that includes other types of practices outside of the traditional ethnographic toolkit and/or rethinking the role of ethnography as our primary methodology.

For context, ethnography has been the primary tool within the discipline for the last several decades. I would define ethnography as a methodological approach that seeks to holistically understand and express the lived experiences of those in a particular sociocultural context(s) (see this article and this paper). Ethnography conventionally entails a specific set of qualitative methodologies that help to understand and analyze these lived experiences, including participant observation, interviews, qualitative coding, and so on. Anthropologists and other ethnographers have built this set of practices because they are excellent at capturing people’s lived experiences, and I agree that they are powerful for that.

I do not, however, believe that these are the only potential ways to do that. For me, ethnography is an orientation, an approach that seeks to make sense of the social world by focusing on the lived experiences of others, not necessarily some collection of qualitative methods. Seeing ethnography as an orientation, for example, would enable ethnographers to use data science and machine learning tools within ethnographies (see this and this).

My perspective here exemplifies the first way some anthropologists have sought to expand beyond the traditional ethnographic toolkit: by redefining ethnography. For us, viewing ethnography as a specific set of qualitative research techniques pigeonholes what ethnography can be. Although these techniques are powerful and useful, their exclusive deployment within anthropology stifles what ethnography can become.

Other anthropologists will seek to expand beyond this toolkit by advocating for non-ethnographic anthropological research. For them, anthropologists should cultivate other research practices in addition to or sometimes instead of ethnography. I am passionate about applying this specifically to data science and machine learning, and Morten Axel Pedersen is a counterpart to me who in this specific area. He thinks anthropologists should move beyond ethnographic research, which could include incorporating data science and machine learning research (see his talk as an example). Similar to me, he wants to see more utilization of data science and machine learning within anthropology, but he presents this as an alternative to doing ethnographic research not as a potential part of ethnographic research like I do.

The difference between the two approaches is subtle: the first advocates for reimaging ethnography and the second for reimagining anthropology and anthropological research while potentially keeping ethnography the same. On a practical level, though, they are not that different. Not only are they not mutually exclusive: one can seek to redefine ethnography and ethnography’s hold within anthropology. But they each also have their place in seeking when encouraging the expansion of the anthropological toolkit. In some situations, the promotion of redefining ethnography beyond its traditional qualitative practices is most beneficial, and other times, advocating for non-ethnographic forms of research would be.

Photo credit #1: StockSnap at https://pixabay.com/photos/people-girls-women-students-2557396/

Photo credit #2: hosny_salah at https://pixabay.com/photos/woman-hijab-worker-factory-worker-5893942/

Photo credit #3: Jack Douglass at https://unsplash.com/photos/ouZAz-3vh7I

What Is the Difference between Anthropology and Ethnography?

(Feel free to check out my follow-up article to this one about rethinking the role of ethnography in anthropology as well.)

A friend recently asked me, “What’s the difference between anthropology and ethnography?” When I tell them I am an anthropologist, people have asked me this question – phrased in slightly different ways – enough times that I am writing this article to answer it for anyone who might be wondering what the difference is.

To situate his question, he explained how other anthropologists he had worked with would often contrast anthropological work with mere ethnography, but that he never understood the difference. That has generally been the experience of people I have talked to who have asked me this question: they have recently encountered anthropologists contrasting their work with other ethnographers, something which left them puzzled given how connected anthropology and ethnography has been in their experience.

Ethnography is anthropology’s “methodological baby,” and in my experience, the anthropology vs ethnography conversation is typically a way for anthropologists to process others’ increasing utilization of ethnography.  Thus, to those looking in from the outside like my friend, this discussion within anthropology about the differences can seem perplexing.

The Short-Answer

book page

The short answer is that anthropology is a discipline while ethnography is a methodology. Anthropology refers to the study of human cultures and humanity in general. Ethnography is a methodological approach to learning about a culture, setting, group, or other context by observing it yourself and/or piecing together the experiences of those there (this article provides an in-depth definition of ethnography).

The field of anthropology has many subdisciplines, ranging from archaeology to linguistics, but in this article, I will focus my discussion on cultural anthropology (the subdiscipline I am a part of). Of all its subdisciplines, cultural anthropology most directly relates to ethnography.

Cultural anthropologists seek to understand contemporary living cultures and societies. They have been instrumental in developing and employing ethnography to understand cultures and other social phenomena. Ethnography has become the most common (but not only) way cultural anthropologists have sought to conduct research.

Thus, the relationship between cultural anthropology and ethnography is that between a discipline and its primary tool that has defined what it means to practice that discipline, like proofs define the field of mathematics or experimentation for the hard sciences.

This sentence sums it up:

In general, cultural anthropologists use ethnography to understand cultures.

It illustrates cultural anthropology’s who, what, and how as a discipline and how each of these key components relates to others. 

There are exceptions to this. Cultural anthropologists do not only use ethnography nor does the word culture describe everything they analyze, but this describes the general relationship between cultural anthropology and ethnography.

This is the short explanation of the difference between anthropology and ethnography. Like textbook explanations, it is accurate but abstract and simplistic. It does not get to the heart of what an anthropologist might be really getting at on when they juxtapose the two. In my experience, when people compare the two, they are reflecting on what they consider anthropological ways of thinking and ethnographic ways of thinking. Hence, here is my long answer, which gets to the bottom of what people are really trying to say.

The Long Answer

There are two angles to consider for the long answer: obstinacy towards others outside anthropology using ethnography and the potential for anthropologists to move beyond traditional ethnography. The former is something we anthropologists must overcome and the latter a set of interesting and innovative prospects for both anthropology and ethnography.

Cultural anthropologists have had a unique relationship with ethnography. The discipline has been instrumental in designing, employing, and promoting the methodology, and with the help of anthropologists, the approach has become a valuable way to understand humans, cultures, and societies. At the same time, ethnography has become increasingly popular in other fields, both academic fields like sociology and political science, and in professional fields like UX research and design, marketing, and organizational management. I think this increasing use of the anthropological tool of ethnography has been marvelous, but multiple disciplines suddenly doing “our thing” has catalyzed identity conflict among some anthropologists.

In my experience, when anthropologists make a sharp distinction between anthropology and ethnography, they are primarily processing this identity conflict. For example, in the ensuring conversation with the person I mentioned in the introduction, I learned that he had recently heard some anthropologists condemn several ethnographies in the field of design where he works as “non-anthropological,” making him wonder what on earth the difference was between being “ethnographic” and “anthropological.” Hence, when I told him I was an anthropologist, he figured he would ask me.

Even if it is at best a historical oversimplification, here is a common narrative I will hear within anthropology: several decades ago, ethnography was the primary domain of anthropologists, but now it seems to be taking on a life of its own, with many others from other fields using it. Others deploying ethnography can have fantastic or horrifying results – and everything in between, but often the implicit and/or explicit assumption in the narrative is that people from other disciplines would generally fail to be able to do as good of a job as a trained anthropologist.

Discussions within anthropology of the similarities and differences between anthropology and ethnography – or between so-called anthropological ways of thinking vs ethnographic ways of thinking, anthropological approaches vs ethnographic approaches, or anthropologists vs ethnographers – have become a major staging ground for processing this seeming recent increase in the popularity of ethnography outside of anthropology.

A few notable perspectives have emerged from these discussions. Some cultural anthropologists promote other methodologies within the discipline either in addition to or instead of ethnographic inquiries (e.g. Arturo Escobar). Others emphasize what anthropologists specifically bring to ethnographic research that others who conduct ethnographic research supposedly cannot (e.g. Tim Ingold). Among the anthropologists I have talked to at least in both the academic and professional settings, I have found the latter to be the most common response: arguing that training in anthropology brings a superior way of thinking about society, cultures, and various social phenomena, which allows trained anthropologists to conduct ethnography better.

Exploring how ethnography might be changing as a wider variety of people use it and anthropologists reflecting on how their discipline has shaped ethnography and ethnography shaped their discipline are commendable. But, this particular way of trying to do both seems like a defensive, “us vs them” response.

In addition to fact that humans seem to very frequently tell themselves “us vs them” narratives, material resources are also at play here. By portraying anthropologists as the only people able to perform “authentic” or “quality” ethnographies, anthropologists can demand competitive resources from potential funders, clients, colleagues, organizations and/or students. This could range from funding for their academic department to being the ones who win the job or contract to conduct qualitative user research at a company.

Whatever factors reinforce this type of defensive response, I believe we anthropologists should instead celebrate the increasing flowering of ethnography and embrace how others might reformulate the methodology to meet their needs. It is an opportunity to crosspollinate and enliven what it means to do ethnography.

A final response by cultural anthropologists has been to rethink traditional ethnography and/or anthropological research itself. For example, Morten Axel Pedersen has argued for a reimagining of what ethnography is in a way that could incorporate data science and machine learning techniques into the ethnographic toolkit and anthropological research (something I have argued for here, here, and here as well). I believe this reassessment of traditional ethnography has a lot of potential for innovative, outside-the-box anthropological research.

Unfortunately, the former chest-pumping explanations of why non-anthropological ethnographies are inferior to our work has been more common than (what I, at least, would consider) this more fruitful conversation. Its bombastic thunder can drawn out the other perspectives.

Conclusion

I can certainly see how non-anthropologists seeking to understand (and maybe employ) ethnography could become confused when they encounter these debates among anthropologists.

To anyone who has been so confused, I hope this article provides – what I see as at least – the wider context for why anthropologists often juxtapose their discipline with ethnography. As anthropologists process how ethnography is increasingly flowering outside of their discipline, I also hope the negative aspects of our response will not turn you away from what is a powerful methodology to understand people, cultures, and societies.

Photo credit #1: Raquel Martínez at https://unsplash.com/photos/SQM0sS0htzw

Photo credit #2: Skitterphoto at https://www.pexels.com/photo/book-page-1005324/

Photo credit #3: klimkin at https://pixabay.com/photos/hand-gift-bouquet-congratulation-1549399/

Photo credit #4: PublicDomainPictures at https://pixabay.com/photos/garden-flowers-butterfly-monarch-17057/

Why Business Anthropologists Should Reconsider Machine Learning

high angle photo of robot
Photo by Alex Knight on Pexels.com

This article is a follow-up to my previous article – “Integrating Ethnography and Data Science” – written specifically for anthropologists and other ethnographers.

As an anthropologist and data scientist, I often feel caught in the middle two distinct warring factions. Anthropologists and data scientists inherited a historic debate between quantitative and qualitative methodologies in social research within modern Western societies. At its core, this debate has centered on the difference between objective, prescriptive, top-downtechniques and subjective, sitautional, flexible, descritpive bottom-up approaches.[i] In this ensuing conflict, quantative research has been demarcated into the top-down faction and qualitative research within the bottom-up faction to the detriment of understanding both properly.

In my experience on both “sides,” I have seen a tendency among anthropologists to lump all quantitative social research as proscriptive and top-down and thus miss the important subtleties within data science and other quantitative techniques. Machine learning techniques within the field are a partial shift towards bottom-up, situational and iterative quantitative analysis, and business anthropologists should explore what data scientists do as a chance to redevelop their relationship with quantitative analysis.

Shifts in Machine Learning

Text Box: Data science is in a uniquely formative and adolescent period.

Shifts within machine learning algorithm development give impetus for incorporating quantitative techniques that are local and interpretive. The debate between top-down vs. bottom-up knowledge production does not need – or at least may no longer need– to divide quantitative and qualitative techniques. Machine learning algorithms “leave open the possibility of situated knowledge production, entangled with narrative,” a clear parallel to qualitative ethnographic techniques.[ii]

At the same time, this shift towards iterative and flexible machine learning techniques is not total within data science: aspects of top-down frameworks remain, in terms of personnel, objectives, habits, strategies, and evaluation criteria. But, seeds of bottom-up thinking definitely exist prominently within data science, with the potential to significantly reshape data science and possibly quantitative analysis in general.

As a discipline, data science is in a uniquely formative and adolescent period, developing into its “standard” practices. This leads to significant fluctuations as the data scientist community defines its methodology. The set of standard practices that we now typically call “traditional” or “standard” statistics, generally taught in introductory statistics courses, developed over a several decade period in the late nineteenth and early twentieth century, especially in Britain.[iii] Connected with recent computer technology, data science is in a similarly formative period right now – developing its standard techniques and ways of thinking. This formative period is a strategic time for anthropologists to encourage bottom-up quantative techniques.

Conclusion

Business anthropologists could and should be instrumental in helping to develop and innovatively utilize these situational and iterative machine learning techniques. This is a strategic time for business anthropologists to do the following:

  1. Immerse themselves into data science and encourage and cultivate bottom-up quantative machine learning techniques within data science
  2. Cultivate and incorporate (when applicable) situational and iterative machine learning approaches in its ethnographies

For both, anthropologists should use the strengths of ethnographic and anthropological thinking to help develop bottom-up machine learning that is grounded in flexible to specific local contexts. Each requires business anthropologists to reexplore their relationship with data science and machine learning instead of treating it as part of an opposing “methodological clan.” [iv]


[i] Nafus, D., & Knox, H. (2018). Ethnography for a Data-Saturated World. Manchester: Manchester University Press, 11-12

[ii] Ibid, 15-17.

[iii] Mackenzie, D. (1981). Statistics in Britain 1865–1930: The Social Construction of Scientific Knowledge. Edinburgh: Edinburgh University Press.

[iv] Seaver, N. (2015). Bastard Algebra. In T. Boellstorff, & B. Maurer, Data, Now Bigger and Better (pp. 27-46). Chicago: Prickly Paradigm Press, 39.