A House for Mr. Biswas

“His household established, Mr. Biswas set about establishing his tyrannies.”

The insight that each household is a little kingdom where the man establishes so many rules, and systems to create, and reinforce his own relative power over his wife and children is brilliant. V. S. Naipaul describes a universal household arrangement. This is how patriarchy works.

Gender-blind Sexism

As I am getting older, I have become more aware of sexist comments or even gender-blind sexist comments that I encounter every once in a while. Before I seemed to purposely tune out of these comments in order to “not rock the boat,” or to be a “productive member of the team.” Now my ears after years of sociological training could not not hear them.

So what is gender-blind sexism? Stoll et al (2016) define it as “as an extension of Bonilla-Silva’s racialized social system theory.” This is still not clear enough. Bonilla-Silva’s colorblind racism thesis argues that color blindness is the new form of racism in United States after the triumph of the Civil Rights. This racism is not overt, but subtle. There might be equality in opportunities, but they do not necessarily translate to equal outcomes based on racial backgrounds. In other words, racism in contemporary America are supposed to be covert instead of overt. Trump’s America, and its overt racism against all people of color have called into question this covert racism idea. Recently sociologist Jennifer Mueller (2017) shows that colorblind racism does not exist as a static stratification system, it requires active and even “innovative” participation of ordinary white individuals to reproduce the same system that upholds their privilege. In other words, colorblind racism as a system has a lot of working mechanisms that we still need to unpack. One thing is clear though which is that it upholds the existing racial hierarchy at the disadvantage of people of color, especially African Americans.

If gender-blind sexism is a framework, being inspired by colorblind sexism, then it should similarly argue that in contemporary American society, and as extension all Western societies, women are supposedly treated as equal as men, but the outcomes might not necessarily be equal. Sexism is more covert than overt. The only person who knows that they have encountered an undignified sexist comments/action are obviously women. If one pushes the argument further by following Jennifer Mueller’s line of inquiry, safeguarding the existing gender hierarchy, or patriarchy requires active and also “innovative” actions from men to reproduce their own power and dominance. This theory sounds very probable to me. But as a person of the female sex, I find this idea terrifying, but so real.

Using this framework, I can then think about situations where my acquaintances, friends and family members would undermine my opinions, sometimes my expertise simply to not have to engage with me. Of course many people around me find me opinionated. Who does not find a sociologist opinionated? Yet, through various experiences when I feel that my dignity as an intellectual, a scholar being undermined, under attacked, and that I am not at all respected, I feel both frustrated, and hurt. What then is the remedy? What can be done about this from a personal perspective? As of now the only person I have been talking deeply about these situations with is my therapist. I spend a set amount of time weekly to analyze these situations, and my feelings around how I have been treated, what I feel about them, and what the solutions can be. Sometimes I speak up. But my faith that male individuals around me would give up their privilege to let me speak, and take into account my opinions is pretty slim. These people have practiced all their lives to not have to take women’s opinions seriously. I cannot remind them every time that my opinions and my feelings matter. After a while, they would simply categorize me as a whiner instead of a problem solver. Over even a longer period of time, they would simply not talk to me altogether because I eventually would appear as “a difficult and uncollaborative person.”

As a logical consequence, gradually I would feel that I would be shut out of conversations, discourses altogether. I would shout in the void if I want to talk to a group of men. I am relegated to only talking to female friends, female colleagues, female co-authors because they understand me, and that we talk in the same language, and we have respect to each other, and we would less likely to dismiss each other’s opinion because no men are in the room. This has been indeed a configuration that I have ended up in most of the time. I went to a women’s college, where no male individual on campus would simply dismiss me. Most of my classmates were female students who care more about the quality of their work than having to compete with another alpha male individual. Then in graduate school, I found myself more likely collaborating with female co-authors. We support each other, and often spend time to both do intellectual work, and emotionally lift each other up. Those are the moments that I cherish.

But when I keep pushing the academic route, I have realized that the further I go, the fewer women I am collaborating with. When I enter into territories where my research collaborators are men, who often claim that they have better technical and mathematical aptitude, I found my opinions bing disregarded, my contributions overlooked. I have to speak up, and emphasize over and over what my contributions are less someone else completely ignores those. When male individuals around me praise each other for very minor help that they gave each other while ignoring my tremendous critique of their work, and my suggestions to how they could resolve logical issues, I feel unacknowledged, defeated, and most of all “exhausted.”

Sociologist Elijah Anderson (2015) coins the term “the white space” to describe public spaces such as neighborhoods, schools, workplaces, etc, which are considered “off limits” for black people. He also points out that white people often avoid “black spaces,” while black people are required to navigate the white space simply for their existence. This idea is powerful, because as a woman I am required to navigate the male space because most institutions were designed by men to serve men to start with. That women have successfully entered them does not mean that those institutions would work for the economic, mental and emotional benefits of women. In many ways, navigating those spaces is terribly exhausting. One can read first hand accounts of women navigating the financial industry, or recently the tech industry. For example, Ellen Pao wrote a powerful memoir that documents her attempt to raise the issue of diversity and inclusion in a Venture Capital firm. She faced a huge backlash, and a lot of professional and personal attacks.

At the end of the day, I realize that micro-interactions show a lot of ideological and structural conditions. My frustrating feeling, and exhaustion dealing with sexism at home, at work, an even at play shows that simply being a female person living in this world is exhausting. It takes a lot of guts, inner strength, self-care, self appreciation, and a strong supporting network to navigate this world. Therefore, if I could make another woman or another non-male person feel good simply because they exist, I already succeed in a way.

Social Positions & Reflexivity

I just finished reading the book Thick by Sociologist Tressie McMillan Cottom. The book is a collection of essays that McMillan Cottom has written over a long period as a public intellectual in both legacy media and new media. She has become a giant in sociology after having won the MacArthur genius award. Having read the book, I understood why she was named “a genius.”

The book is not a scholarly research. It is a popular book written with sociological sensibilities. It makes me appreciate my profession even more. It uses social theory to explain popular culture. I wish I can think, live, breath, talk and speak like a sociologist like what McMillan Cottom does. I rarely meet a sociologist who actually lives sociologically. But I think McMillan Cottom does.

The part that struck me the most in the book is her reflections on the differences between her social position, and that of her grandmother. This shows her sociological reflexivity in using social theory to explain differences in outcomes of two women in the same households growing up and growing old in different America’s.

I have always tried to distance myself from using social theory in examining my own family. I run away from such endeavor because I was afraid that I would open a can of worms of pain, trauma, shame, guilt, remorse, and also silences in my own household. However, this realization begs me to consider the question: at what point would I gain enough confidence and honesty to critically look at my own personal history, and history of my people, the Vietnamese who came from đồng bằng Bắc Bộ, or the Red River Delta, or people from small towns in Northern Vietnam.

Do these questions constitute a line of brave, original, and worthy inquiry? Or are they simply questions that help me to come to terms with who I am as a person, a scholar, and a sociologist?

What does it mean for me to observe how the women in my household being humiliated by their husbands in public? How have these instances create an understanding of gender relations now and then? What about being told repeatedly in academia that my research questions are too narrowed? How does it affect my self-esteem, and my confidence in writing, doing research, and finding intellectual not only fulfilling but also joyful?

There are many questions to ponder upon. Maybe I should follow McMillan Cottom’s genre: personal essays to unearth these questions, the questions that I never dare to answer honestly.

Pachinko by Min Jin Lee

I finished reading the novel Pachinko last month. After putting the book away, I started to miss the characters in the book. I still want to know more about Solomon’s life as a Pachinko store owner after he inherits his father’s stores. The characters in the novel appear as if I know them. Their personalities are so crystal clear that I cannot mistake them for someone else. Min Jin Lee has a gift. She is able to not only construct fictional worlds that are so real, but also to construct fictional characters that you thought you know them all a long. Furthermore, using her gift as a writer, she shows the reader what discrimination feels like, tastes like, and experienced like.

The worlds in Pachinko remind me of a passage written by sociologist McMillan Cottom:


But Sociology comes as close to the core of where my essays start as anything else I have explored. Drawing on what ethnographers have called thick description, I finally found a label as complex as my way of thinking. I take very seriously the idea of social locations. We are people with free will, circumscribed to different degrees by histories that shape who we are allowed to become.

Min Jin Lee is attentive to each character’s social position. Three generations of a Korean/Korean Japanese family starts their lives in Japan relying on the business of running Pachinko, and even after three generations with money, their social positions remain the same, that is they are still stuck with the pachinko business.

Novelists practically use thick description. I wonder when Anthropologist Clifford Geertz was inspired by how novelists created fictional worlds that are even more real than the real world. Maybe that is why he was able to describe a cock fight in Bali that is so real to any reader.

Lee’s seemingly central thesis of the Pachinko novel, if there is such an argument in a novel, is that social positions determine a lot of the outcomes in life. An individual can try to exercise their agency, their free will. The outcome might not necessarily what they want to admit that despite their trying, they end up doing what the society has already pre-determined that it would be eventually what they do.

This realization saddens me. What is then to be done about discrimination, prejudice, stereotypes, and all the microaggressions that a minority person has to experience. They put up a good fight, stay silent for nothing? What is it at the end of the tunnel for such a person? Or which society would be most tolerant for such a person?

November Reading List

In October, I read quite a few novels. Reading novels is like trying to get at emotions that social science writings can never get at. Novelists think very deeply and carefully about crafting sentences, descriptions, and evoking sentiments that words themselves don’t sufficiently capture. For the month of November, I am trying to read a few novels, and a few social science books that help my research. Following is my list:

Omniscient Narrator: Free Food for Millionaires

I can’t say how much I like Min Jin Lee’s novels. I have devoured one novel after another in the past one month. I put aside scholarly reading to immerse in the fictional worlds that Lee created. I started out with Pachinko, a book about three generations of Korean Japanese experience in Japan. Then Free Food for Millionaires naturally rose up a few notches in my reading list. I felt guilty for not reading for scholarly endeavors. Yet I felt extremely happy, and fulfilled because Lee has opened doors to so many worlds, both familiar and foreign to me at the same time.

If I could, I would write books like Min Jin Lee. There are deep insights into human society, and how people behave under constraints in Min’s stories. Stylistically, I like her omnipresent, omniscient narrating. In her own words: “There’s a godlike quality to omniscience, and it is what I am vainly approaching in storytelling.” An all-knowing-narrator sounds powerful. This entity gives voices to everything, everyone, and let the reader know so much more than what the characters themselves can express.

Though omniscient narration is an unpopular way of storytelling for modern writers, it can reveal how everyone in the room is thinking about the issues and each other and themselves, rather than what they are actually doing and saying. Even the people of the finest characters don’t speak truthfully or act honestly all the time. It’s only in fiction that all the dimensions of personality and behavior may be witnessed. I wanted to have a go at taking it all down.

Lee’s description of her technique makes me wonder whether I can write sociological books this way. Can I play God in my scientific work? Can I be that all-knowing narrator to attribute motives, and telling a background story of every character, and analyze their actions sociologically? I want to copy Lee’s prose in my creative writing projects. More than anything else, I wan to emulate Lee’s writing philosophy in my scientific work.

Anxiety while Reading Social Theory

I have to admit I’m a shallow, and lazy reader. I prefer reading texts that are simple, and clear in prose. My most recent read that I cherished, and learned so much from was Ellen Pao’s memoir, Reset. The narrative is relatively linear. Everything is told in a chronological manner. Ellen Pao is a to-the-point storyteller. The messages are straightforward enough that I did not have to read the book twice to get any deeper meaning of life. The main message is that the tech industry has a deep structural problem in lacking diversity, and it has been paying only lip service to improve the situation. The solution is that VCs and tech CEOs should be agents of change, by creating and shaping inclusive organization policies and cultures.

Now I am in the process of writing my dissertation, I have bigger theoretical, and philosophical questions that require close readings of certain philosophical texts. There’s no reason why I should not set time aside to do these readings. I ought to read them at some point, and I have decided that now is the time for me to engage with social theory.

While contemplating about the different epistemological worlds that the left and the right in the United States are living in right now, I was recommended to read Black Feminist Thought by Patricia Hill Collins, and The Racial Contract by Charles Mills. I was elated at the challenge. Yet I have to admit that my anxiety and fear of philosophy, and social theory did give me some reservations about how much from the texts I would understand, appreciate, and be able to engage with.

Regardless, I am giving these texts a read, and will document my reactions toward them in the next few blog posts.

Production of culture

I am copy-ing this list as a reading list for cultural production.

Code and Culture

[Below is a recent list Peterson wrote outlining the production of culture perspective. You can view it as an update to his ARS with N Anand. Pete wrote it to accompany a talk he gave and circulated it to some friends. I copy-edited/tagged it and am posting it with permission. If you know links for any of the non-tagged citations email me or put them in the comments and I will update the post. –Gabriel]

| Richard A. Peterson |

Examples of works written in the spirit of the Production of Culture Perspective

Created for the working conference
Euro-Pop: The Production and Consumption of a European Culture
Villa Vigoni, Lake Como, Italy 9-10 June, 2009

Richard A. Peterson

A. The production of culture perspective focuses on the ways in which the content of symbolic elements of culture are shaped by the systems within which they are created, distributed, evaluated, taught…

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Red Pill Blue Pill by David Neiwert

David Neiwert examines “new conspiracism” in the age of Web 2.0, and how it is linked to online radicalization. Following are a few paragraphs about epistemological bubbles that have emerged on the Internet in the past 30 years:

Like the hypercool hero Neo in the films, true believers in the alternative universe of conspiracy theories are absolutely convinced that the epistemological bubble into which they have submerged themselves is the real reality.

The red-pill metaphor is a very provocative, and effective metaphor for people to be attracted to, and used when they transition to an epistemological bubble of the extreme right.

The universe of conspiracy theory constantly recruits new followers on the Internet. Somehow, this movement sounds like a spread of a new religious movement:

Sorting out good information from bad has become seemingly an overwhelming task in the age of the Internet and social media. Some people have stopped trying. Others have embraced the abyss, as it were, by diving into the epistemologically malleable and manipulable world of conspiracy theories, a zone where normative rules of evidence and factuality need not apply.

October Reading List

I thought that I would have read a lot during the month of September. It simply was not true. Reading is labor for an academic (more precisely for an aspiring academic). I finished reading half of the books in my September reading list, and was also able to write a summary of one book for my dissertation project. That was an accomplishment. Being engaged with a scholarly book in reading and writing is a very labor intensive activity. To keep the momentum going, I will be reading the following books in the month of October, or more like the remaining two weeks of October.

The books are organized around four themes: platform content moderation, racism, diversity in tech, and organization studies.

Dying of Whiteness

This books aims to examine:

how particular American notions of whiteness—notions shaped by politics and policies as well as by institutions, history, media, economics, and personal identities—threaten white well-being.

White Habitus – Racism without Racists

In his seminal book Racism without Racists (2006), Eduardo Bonilla-Silva defines the concept “white habitus” as “a racialized, uninterrupted socialization process that conditions and creates whites’ racial taste, perceptions, feelings, and emotions and their views on racial matters.” This concept explains certain idea of white solidarity, and white identity that I am currently trying to understand in the American context.

Bonilla-Silva goes on to explain: “One of the central consequences of the white habitus is that it promotes a sense of group belonging (a white culture of solidarity) and negative views about nonwhites.” Scholars often stay away from discussing the effects of active social isolation from ethnic minorities by white Americans. Bonilla-Silva doesn’t shy away from such a difficult topic. He points out that this kind of habitus develops a sense of identity, a sense of belonging, and a shared experience. Further exploration of white solidarity and identity in contemporary America under Trump is a difficult task. In the past two months of my reading on the subject, the book that deals the best with this topic is Dying of Whiteness by Jonathan Metzl.

September Reading List

I am not going out to restaurants, I also not using the subway to travel anywhere. The COVID-19 Pandemic has helped me save some money. I spent this ear-marked money on books. Because of work from home, and school’s work from home policies, access to physical books has become very limited to me. I don’t do well with e-books. Reading e-books does not help me retain information. Words come in and out immediately whenever I read things on my computer screen. I need to hold a physical book in order to think slowly what the author means.

Following is my reading list for the last 10 days of September: Dying of Whiteness by Jonathan Metzl, Red Pill, Blue Pill by David Neiwert, Talk of Love by Ann Swidler, Summoned by Iddo Tavory, and The Age of Surveillance Capitalism by Shoshana Zuboff. These books have been suggested to me by colleagues, friends, or advisors. They are fall broadly under three categories: whiteness studies, ethnography/cultural studies, and digital capitalism. I still read scholarly articles for information, and research. Yet when it comes to reading for critical engagement, research and pleasure, I still prefer books. I am not entirely sure my dissertation will turn into a book, but I cannot deny the appeal of having my name on a monograph published by an academic publisher. Let’s wait for a couple of years to see where and in what format I will end up publishing my research.

Book Review: Ghost Work

I have planned to read Ghost Work by Mary Gray and Siddharth Suri for a long time. I bought the book when I visited a talk by Mary Gray at Data and Society about a year ago. Since then the book still sits on my bookshelf. The general theme of the book is about the necessary unseen human labor behind the seamless automated systems. The core concept of the book is the idea “paradox of automation’s last mile.” It refers to the phenomenon whereby when Artificial Intelligence becomes more advanced, it would create “temporary labor markets” to solve “unforeseen and unpredictable tasks.” Furthermore, the grater paradox of automation is that “the desire to eliminate human labor always generate new tasks for humans.” In other words, automation cannot and would not in a foreseeable future, rather automation reconfigures work, and reorganizes human input in the production process.

In the authors’ own words, on-demand service work is necessary because we do not know when machines need human input:

“As machines get more powerful and algorithms take over more and more problems, we know from past advances in natural language processing and image recognition that industries will continue to identify new problems to tackle. Thus, there is an ever moving frontier between what machines can and cannot solve. We call this the paradox of automation’s last mile: as machine progress, the opportunity to automate something else appears on the horizon. This process constantly repeats, resulting in the expansion of automation through the perpetual creation and destruction of labor markets for new types of human labor. In other words, as machines solve more and more problems, we continue to identify needs for augmenting rather than replacing human effort. This paradox explains why on-demand services – commercial ventures that combine humans and APIs to source, schedule, and deliver access to goods and services – are more likely to dominate the future of work than AI and bots alone (p.176).

The authors interview workers in the United States and India, and bring them to us. They go behind the API curtain, and reveal to us who the workers are, what are their aspirations, and why they work for on demand platforms. This book is comparative on many levels. First, it compares workers in different countries: The US. and India. Second it compares experiences of workers who work for multiple platforms: Amazon Mechanical Turk, Microsoft Internal On-Demand platform, Amara translation service, and LeadGenius. The diversity of platforms, and countries allow us to see a picture of an emerging global on-demand labor market, which performs million of tasks, which vary in complexity. This book therefore enables us to see what is often hidden and abstract.

The authors then provide institutional background on why the on-demand labor market has become necessary in the digital revolution. They also show us how on-demand labor market is not a new system. Before the industrial revolution, many women and households took on the job of sewing the last buttons to clothes before the textile industry figured out how to automate the process, and confined the work within the walls of garment companies. This inclusion of non-institutional labor is important in the process of automation.

As they walk us through lived experiences of their interview subjects, the reader recognizes heterogeneity of human labors in this on-demand market. What becomes apparent is that since the market does not have a clear requirement of educational background, training level, etc, it allows for a diverse labor force with different levels of training, different levels of education, and coming from diverse racial, ethic, and religious backgrounds. However, this heterogeneity of labor supply, and diverse forms of tasks also create inefficiency, and friction because workers have different levels of skills, and that requesters/ employers have to define the tasks themselves. The authors show that this system involves a lot of transaction costs for both workers and employers. The two sides have to put in the time to find the right match, and to explain to each side how to do the task as intended. Workers avoid the problem of looking for tasks by creating social networks outside of the platform. They rely on social media, online forums to find the right tasks. As the author outlines this problem of transaction costs, I wonder whether building a well thought-out communication platforms for on-demand workers and employers would be a potential solution for the various transaction cost problem in this market. This is a technical solution for the current inefficiencies in this market.

One theme that I observe in this book with other Gig economy books that I have read in the past year is that workers in this economy are subject to algorithmic arbitrariness. Workers are suspended, and kicked out of the platforms sometimes randomly, and sometimes according to rules that are not taking their real life situations into account, while workers have no recourse, no where to complain. This shows the power of platforms over workers, and that workers though important to platforms’ profitability are not treated as assets but expandable number that could be eliminated at will.

Gray and Suri explain:

“The worst expression of algorithmic cruelty is disenfranchisement. Under the guise of safety, systems designers make it easy to block or remove an account in case a bad actor tries to cheat the system. This adversarial stance means that good workers are sometimes misinterpreted as shady players. Inevitably, mistakes are made. A worker changes an address, loses her internet connection, or shares an IP address with another worker. Each one of these things is potential red flag. The algorithmic system sees the flag as a possible security threat and, with no one at the helm to distinguish friend from foe, the worker is penalized. The penalty may look like being blocked or suspended, or having an account deactivated. Again, in an ecosystem in which workers are seen as interchangeable, the system automatically eliminates what it deems bad apples. The sad irony is that even the best – intentioned and most seasoned workers can get caught in the dragnet.” 86

Workers are dehumanized through the process of de-identification. Mturkers become lists of numbers. This reminds me of how Jewish prisoners given a number during the Holocaust. Giving a working human being a code to interact with is so dehumanizing for both sides: the requesters, and the Mturkers. The authors though qualify this statement by saying that in case of workers who come from discriminated classes (gender, religion, etc), not being identified by names and gender sometimes giving them advantage.

At the end, I feel that the book presents a good narrative of what is going on in the tech economy. However, as a sociologist of work, one question remains unanswered is the question of “work process” among on-demand gig workers: Why do they work so hard for very little paid, and why don’t they quit? What is the average tenure of an on-demand gig worker working for an on-demand platform? The authors point out the 80/20 Pareto rule to create a typology of three group of workers. However, I want to know among those who make on-demand work their full time career, why do they work so hard for little pay? Another question is why they not call them gig workers? What is then the difference between gig work and on-demand work? Aren’t they the same?

To answer the question what keeps they in the game, the authors provide a partial answer: many of them are in the game for the cognitive benefit of it. They learn new things, keep up their skills (most of these answers come from Indian subjects). However, my sense is that because the book is not an ethnographic research, they can never quite get at the process that workers rationalize the decision to remain in an exploitative labor scheme.

Besides, How about their American counterparts? Why are they working so hard for little pay? The answers are either implicit or not satisfactory. Implicit in the sense that they work for various reasons. One, the workers population are so heterogeneous, they should have different reasons why they work in this sector. Thus, they should also have different reasons why they stay. Is there anything about the on-demand aspect of this that keeps them stay? Is there anything about the brandname (Amazon.com, or Microsoft) that make them stay? These questions remain open.

Finally, as a methodology enthusiast, I feel the book to be not transparent in its methodology. Who were involved in the interviewing process, who was contacted, who was doing the interview, etc. These pieces of information is absent. As mentioned earlier, because the book is not explicit in whether ethnography was involved at all, readers cannot really picture the embodiment aspect of online/on-demand work.

Because I care so much about reproducibility of research, the book does not have a methodological appendix that makes me cringe. I know that it is produced for popular audience, but as a scholar, a researcher, a scientist, I want to know how many people they have interviewed, how did they interview them, how many in person, how many remote. How did they avoid positionality biases being MSR employers, privileged, and at times employers of those ghost workers.

Overall, I agree with the authors that there’s a global ghost work sector that is increasing in size because of the increase in demand for human in the loop tasks from various tech companies. They are working outside of the formal employment structure, and they are subject to the whim of the platforms, and being exploited by requesters because of the platform design. However, I think the book has not answered many questions, and one of which is methodological, and another is theoretical.

Despite many questions that I have, the book is a starting point of a long over-due conversation: who are the human workers who power machines. How can we as society protect them, and enable their creativity for our better future. The book is both practical, and hopeful that we actually will continue to need humans in the loop. The book also provides one practical solution for job training program at the city level that I really like: supporting public education, and letting residents to take college classes that they would want to take in order to benefit their work. This similar program enabled me to audit courses at Humboldt University, Free University and Goettingen University during my stay in Germany. It plugged me into the intellectual environments of those excellent public universities, and through those courses I had also made long lasting friendships. I’m all for investing in public universities and making their courses available to those who pay their taxes to support such excellent public education.

Book review: After the Gig

This semester, I am teaching the class The Sociology of the Gig Economy at Hunter College. This is a master’s level class where graduate students in social science research, and honors undergraduate students will explore various issues of the gig economy. I am pretty excited about the content of the class. After our first meeting last week, I have become even more excited about the participants. Throughout the semester, students and I will engage in a few public pedagogy projects whereby we produce content and knowledge for public consumption. This is my first time experimenting with such an idea. I think there will be challenges, but hopefully we’ll be able to create solid content for public consumption.

In the process of preparing for the class, I have ordered like 20 different new books in the summer. Most recently I finished reading the book After the Gig by Juliet Schor. As the name suggests, it is a book about the gig economy.

I categorize this book as an empirical examination of the gig economy from the sharing economy point of view. This book is on my bookshelf physically placed next to Uberland by Alex Rosenblat, and Hustle and Gig by Alexandrea Ravenelle. I have reviewed Uberland for Sociological Forum, and really appreciated the book’s approachable language. Alex Rosenblat does not use heavy theoretical language to make her point across. That is Uber drivers come from a diverse backgrounds, who have different reasons why they become taxi drivers. Yet she’s able to show that over time, Uber has engaged in shady practices to increase surveillance and control over its workers, its customers, and critics like herself. When it comes to Hustle and Gig, I appreciate Ravenelle’s clear argument: that is, in the gig economy, companies shift risks onto workers. And her solution to this risk shifting problem is to advocate for changes in the independent contractor category. The government needs to make gig companies recognize these workers as their workers. So instead of getting a 1099 form, these workers should get a W2 form like other “organization men” in William Whyte’s words.

How is After the Gig different from the other two gig economy books that were also published by University of California Press? I think the answer has to do with its approach, scope, and the consumption aspect.

First, Juliet Schor approached the gig economy phenomenon from the sharing economy point of view. That is, she used the consumption, anti-capitalist discourse of the gig/platform economy as the spring board. For example, throughout the book the idealist discourse is being problematized. This discourse makes the argument that the sharing economy promotes collaborative consumption, environmental conservation, and financial independence. While the other books I mentioned above focus exclusively on the workers and how platforms use data and algorithms to discipline workers, this book looks at other aspects of the platform economy: collaborative consumption, environmental conservation and then economic gains for workers.

Second, this book relies on data collected by a team of researchers that look at many for profit and non-profit platforms. This is a marked research design difference from the other two research projects. Trained as an economist, Juliet Schor is able to show the reader what the economics of the platforms is. I really appreciate her non-jargon explanation of how economics works in this economy. In order to keep workers poor and dependent on platforms, Schor argues that we need to understand two important concepts: algorithmic control and policies of precarity.

What is algorithmic control?

To some extent, algorithms are self-learning entities that change without human intervention. But on labor platforms they are also paired with policy decisions made by real people.

In other words, platforms use both automation (algorithms), and policy decision making to discipline workers. While it takes almost nothing to start on any platform (Uber, Taskrabbit), platforms can fire workers anytime (through deactivation mechanism). This high cost of job loss is really high for gig workers.

Schor and her team argue that “platforms have ushered in fundamental changes in the organization of work.” They are parasites, who do not pay tax, and just use public resources (roads, etc). They subsidize consumers through venture capital money, and then compete with public services (public transportations).

Similar to what Alex Rosenblat’s argued in her book, Schor also argues that the platform economy has ushered in a new labor regime. Specifically, we observe a retreat from control, or direct-human control. Employers allow for a wide range of work hours, a wide range of workers with different educational backgrounds, etc. Similar to historian Louis Hyman, and communications scholar Mary Gray, Schor also highlights the similarity between this system and the pre-factory era home-based “putting out” system. Platforms as accepting more heterogeneity among its workers allow for a more diverse workforce. Yet, this also means that we’re facing with more inequality within this economy.

Finally, Schor examines a few case studies of non-profit sharing platforms, and shows the readers why they fail, and how they fail. She argues that sometime the setup lacks “a value proposition” and operates based on “ideological commitment.” In other words, their economic activities appear to be not durable, and would soon fail when economic situations change, and other social dynamics (such as status positioning) kick in.

In conclusion, Schor documents the rise of commercial platforms, and attributes their growth to the fact that they have offered something of significant value to users: consumers get lower prices, and providers get extra income with flexibility. However, looking at consumers and providers alone is not enough. The platforms have plenty to gain from these activities such as power, and consumers’ data. Thus Schor calls for more regulations in this market in order to protect consumers, providers, and society as a whole.

Baking and Culture of Measurement

I have been obsessed with baking Asian cakes such as mooncakes, anpan, Hokkaido bread lately. My tiny New York City apartment kitchen has been filled with baking ingredients and tools including five different types of flour, different molds for different cakes and bread. The basic equipment and ingredients are readily available in my home.

However when I started making mooncakes for example, I ran into the problem of recipes. In order to get a hang of baking techniques, I often go to Youtube, and observe how other people from different countries make mooncakes, and Hokkaido bread. Once I read a few blog posts, and watch a few videos, I seem to get a conceptual hang of the workflow, and feel that I can comfortably make a new type of bread without much difficulty. However, people often say that baking is a science. That means, what determines whether a cake is a success or not lies in the precise measurement. This I found to be a troubling issue especially when making Vietnamese cakes.

I found recipes in Vietnamese on the Internet to be very underwhelming. Most of the time, the measurements are not precise, which throw me off. Whenever I found a ciabatta recipe for example, the instruction is full with details that I feel happy about actually not reading the extra story that the writer tags along to personalize the food making experience. I would go straight to the end of the blog post, look at the recipe, get a general idea of the workflow, then I would go to Youtube and find videos to see how the recipe actually is executed, and certain steps that could never be verbalized in writing.

This general workflow helps me with many cuisines: Chinese, German, American, Mexican and Mediterranean. But when it comes to Vietnamese food particularly Vietnamese recipes that I remember as kid growing up in Vietnam, I find lots of frustration. I often find the writing to be dry, not detailed enough, and it leaves me with an unsatisfactory feeling that the author does not try to make sure that I’d be able to re-create the same experience. This realization made me think about a culture of writing cookbooks, recipes, and blogs. Each recipe takes a lot of care to master, and then to write a blog post to explain what one does. This is a lot of labor and care. What sets the Vietnamese recipes and Western cuisines recipes apart for me now is this level of care, level of appreciation.

I believe that there are many Vietnamese recipes out there that people need to try. Yet, in order to figure out what they are, one needs a class of cultural producers who would be able to introduce these different recipes online, and then popularize it in the world. This is such a cool idea for a Youtube channel, and food blog. I hope that a class of young talented Vietnamese people out there are doing precisely this: to make sure that Vietnamese recipes are accessible to the culinary world, and treat Vietnamese foods with care, and patience.

Topic Modeling makes Bayesian Cool

I have been obsessed with topic modeling for more than a year now. It is an NLP technique that actually has important applications in social science research. This is a big feast for computational methods, and for a social scientist like me.

When I first learned about topic modeling, I spent a lot of time trying to learn how to make it work. Besides, I also wanted to know how I can use this cool technique in my research concerning race/ethnicity, immigration, etc. I was not concerned at all about the mathematical underpinning of the method.

This all went well until I learned Bayesian statistics this summer. Now I see Bayesian everywhere. I finally understand that under the hood, Bayesian inference makes topic modeling such as LDA, or STM possible. This eureka moment really elated me.

It turns out that I have been using language of tuning hyper parameters without really understanding what goes underneath the entire process. Now with some basic Bayesian statistics, things start to make more sense to me, and I feel more confident in explaining how topic modeling works.

Baking is a science

People say baking is a science, and cooking is an art. I enjoy baking much more than cooking because oftentimes there are precise description, and precise measurement of how much ingredients and materials one should prepare before making a cake. Until I tried to make moon cakes, a type of cakes that many East Asian countries eat during the Moon Festival. It’s my childhood favorite, and I miss eating the kind that one can only find in Vietnam (bánh thập cẩm, or mixed flavor).

I followed a few recipes found on the Internet and Youtube. Results have turned out to be dried, and not so pretty.

Then I tried my hands in making mochi ice cream. It’s even more difficult.

Mochi Ice Cream - Kirbie's Cravings
Picture: Kirbie’s cravings

What I figured out during the two failed experiments is that making Asian cakes is so much more complicated that I had thought. I expected that unlike making a croissant or tarte tatin, I at least have an idea how they should taste like at the end. As a born and raised Vietnamese, at least I have the moral authority to say that the cake I make taste like what Vietnamese people in general would consider good. What I recognized at the end though making these cakes has less to do with measurements, ingredients. It has a lot more to do with techniques, equipments, and whether one has a clear expectation how the final products should taste like. So in many ways, baking is also an art. Making these cakes is more like cooking a bowl of Pho than making a tres leches cake.

These Asian goodies are supposed to be moist, soft, and delicate. They are not supposed to be chewy or fluffy. They cannot be made using a ready made cake mix. They take a lot of time to make, and the process is pretty involved. One cannot cut corner and expect the output to be pretty or tasty.

This baking process reminds me of the scientific endeavor that I am engaging in at this moment. When one embarks in a research project, one thinks that they have a clear idea what to do, until they figure out that there are many steps in between. Then they become confused, and frustrated. The science of baking, and making pastry is not opaque especially in the Internet era where many recipes are available for consultation. The opaque part lies in the details how one should mix what kind of flour, and which one goes into the oven first, and how much egg coating should one put on top of the delicate almost ready mooncake.

These baking experiments made me realize that in doing any project, patience is key, and that figuring out the perfect procedure takes many trials and errors. Translating it to doing science, maybe writing papers over and over again helps one write better papers. Maybe having one solid idea and then translating it to written words would eventually become easy once I figure out how to not cut corner?

Applications of Bayesian Thinking

I struggled with understanding how to use Bayesian statistics in my professional work. More specifically, I am struggling understanding the building blocks of Bayesian statistics. What I do see is that Bayesian statistics involves probabilistic thinking, and very clever sampling of data distribution. On the higher level conceptualization, the ideas of prior belief, and updating your belief to get a posterior estimation are intuitively appealing. However, when it comes to mechanics of applying Bayesian logics, I am struggling a lot.

This struggle does not deter me from enjoying reading about Bayesian logics being applied in real life situations. Recently I listened to a Data Skeptic podcast episode about data representations, data visualization, and how we citizens or audience are unconsciously being educated about statistics through reading and interacting with very well-thought out work of data journalists and data scientists at news organizations. The specific example that professor Jessica Hullman mentioned is an example from the New York Times’ interactive graphic representations of inequality in America. The interactive exercise asks the reader to provide their prior belief about inequality in America, and then presenting them with data, thus nudging them toward updating their belief. This is such a brilliant statistics exercise from a very well-respected news organization in the US. I wish that more news organizations in the world exercise what the New York Times does: using data journalism to educate the public about social issues in a scientific way.

For those who would want to listen to the podcast episode, it could be found here. Let me know what you think about Bayesian statistics, and how one can implement Bayesian statistics in social sciences.

What Journal to Publish in?

My mentor often says that before submitting a paper for publication to a journal, one has to do thorough “market research,” or to have a general understanding of what the journal is about. My understanding is that each journal is a cultural institution, and the job of a researcher is to make explicit those cultural norms. Relying on this explicit knowledge, they could make a more informed decision about the venue.

A friend came to me today with a set of related questions:

Where does one look to find out simply what the background of a journal is — which discipline(s) it covers, how long it’s been around, what its mission is?

I thought about these questions for a while, and came up with a 6-step procedure to figure out how one should categorize a journal:

  1. Read the journal’s self-description
  2. Read the Wikipedia’s page of the journal
  3. Examine the chief editor’s profile. The chief editor’s background is indicative of who the potential authors and the audience should be.
  4. Examine a few articles, and see who are the authors
  5. Use the advanced search function on Google scholar, and find articles published in the journal, read the articles titles published by the journal.
  6. When the journal is interdisciplinary, look at a few issues to see which disciplines the authors come from.

This might be different from how other academics do their “market research.” I’d be interested in learning more about how other people decide where they submit their work, and why they make such decision.

Bayesian Struggle

Bayesian Fun
Picture credit: GA Tech

I am currently attending a statistics summer school at ICPSR (University of Michigan), one of the most renown methods training grounds for social scientists. I had applied to the summer program before, but never participated because I could not afford it, and that I did not really want to go to Ann Arbor in the summer. But this year, amidst the Covid19 Pandemic, ICPSR became a possibility at least financially, and also due to its online format I never have to go anywhere outside of my apartment to attend it.

My experience has been great so far. I’ve got to meet with really smart and self-driven PhD students, researchers, and professors from all over the world. This is really exciting. We have been doing trivia on Zoom, swapping tips, tricks and talking about each other research interests on Slack.

My enthusiasm to learn more stats got curbed immediately when the Bayesian statistics class started. The reason that I signed up for the class was simply that I’m interested, and I want to know more about probability, which I never formally learned as an undergraduate student, and is currently not offered in my graduate program. I soon recognized though that the class has a lot of formal mathematical proofs, and probabilistic theories, and math axioms, something that I missed doing, yet my math knowledge not quite sufficient to do abstract derivatives. I forgot most of my linear algebra, and differential equations after almost a decade doing ethnographic work, and reading social theory.

The class started from the simply and elegant Bayesian theorem:

Use Bayes' Theorem to Investigate Food Allergies — Count Bayesie
Photo credit: http://www.countbayesie.com

It then became a full length discussion on how to use different priors, different sampling techniques, and how clever mathematical manipulation can get you very far. My head is been spinning, and I feel seriously doubtful of my intellectual ability.

One good thing that comes out of this experience is that I have been reading a lot more because Bayesian logics does not appear intuitive to me. I’ve been watching a lot of Youtube videos about Bayesian applications. I am curious at what point this world view would make sense to me.

To get a sense of what this Bayesian world feels like, and thinks like, I watched this funny, and engaging YouTube video:

The presenter makes it feel so easy. She naturally incorporates concepts such as conditional probability of A given B, and the probability of A, or of B in her speech. It feels as though she lives her life in a Bayesian way, and thinks like a Bayesian. Then she provides example of how a person would be able to use the Bayesian theorem to calculate the probability that their blind date would share their interest in Star Wars given what they already knew about the world. This sounds very nerdy, but it works for some people. I wonder how many people actually think like this. Certainly I am not one, and I am also not surrounded by Bayesian thinkers on a daily basis.

Ok this video makes Bayesian interesting, and applicable in real life. How about moving a little bit further? I have not been able to understand all the assumptions, and sampling techniques in doing Bayesian statistics, but probably I can ask relevant questions that highly technical people could answer. The whirlwind of deep learning seems to be sweeping every corner of the scientific world, how do Bayesian statisticians, and applied Bayesian people survive and adapt? I found that Bayesian people are making themselves relevant, and pitching their work to the deep learning and machine learning communities. For example, most recently at NeurIPS 2019, one of the most important machine learning conferences, there was a workshop called Bayesian Deep Learning, where presenters and speakers pitched various ideas about how Bayesian statistics is relevant to machine learning, and vice versa. This is very exciting. So Bayesian statistics is not a dying field that I’m stumbling into now. It’s evolving as the field of statistics is getting more and more exciting.

I still have a lot to learn during this summer program. It feels very bizarre to go back to do derivatives, and doing matrix algebra. Yet, I am excited about learning new things. Math always makes my brain hurt. But like one of my math professors often said: “Doing math is joyful thinking.” I should enjoy this process of writing and following proofs. Sometimes I even wondered had I remained doing math since college, what would I have become?

Deep Listening: Engaging with Respect

When I first started college in the United States, I struggled. I struggled with English because as a non-native speaker, the first year was very difficult. Every student had to take English 101, first year seminar, major, and minor required classes. Even before school started, the incoming freshmen were asked to read a non-fiction book, and created original and innovative responses to it. I was overwhelmed. Nobody had ever taught me how to react to a non-fiction book. What was expected of me? How would the deliverable look like? I performed well if there’s a clear guideline of what deliverable I should provide. But when the deliverable is anything possible, I became paralyzed.

I arrived in college with the feeling that I was insufficient because I could not produce a creative response to a non-fiction book. Then in the first orientation week, I saw my peer products displayed at the fine arts department, I felt both awed by what they were able to create, and felt inadequate because I was not creative, and that I failed to bring with me any artifact. I was trying to figure out the American college system, and American higher education culture.

Then English 101, a required class, started. I was both excited, but nervous, and sometimes dreaded that I had to go to English classes. As a high school student, I never excelled in English, or Vietnamese. I never got good grades in these subjects, or felt the urge to write any poem, or wrote a good literary analysis. My high school writing was mediocre but logical. I often got away with writing a very dry essay that hit all the points instead of writing a flowery essay that makes the reader feel good. I kept the same attitude toward English, or maybe the fear of humanities subjects when college started. My English professor also looked very strict, and the readings were very foreign to me! We read Othello, old English poems, and Tony Morrison’s Beloved. I had virtually no cultural background to be able to comprehend the texts. My only tool was to pretend that literature speaks to all humanity regardless of the reader’s race, ethnicity, cultural background and lived experiences. Later on, I learned that this assumption was very wrong.

English 101 was the class that took most of my time, yet I felt really inadequate in it most of the time. Until one day, I had to make a presentation about a reading in class. I don’t remember what the presentation about any more. The only thing I remember now is that it was a 10-minute presentation about an author, and their work. My job was to summarize the author’s life, and their literary works. The only and the most important thing I remember though is the feeling that I had during and after the presentation. My peers were listening to me very attentively, and asked questions after I presented. I was of course nervous, but they were all paying attention to me, my powerpoint, and did not care much about my broken English. I felt empowered. I felt respected. I felt engaged. It was definitely the first time that I recognized the power of being listened to. My expectation was that nobody would listen to my presentation because I did not really know what I was doing. I also never took English seriously. Yet the fact that my peers and my professor took me, the topic and the subject matter seriously, I felt elated.

That moment of feeling respected, recognized, and centered was decidedly a turning point in my approach toward higher learning. It was also the moment when I recognized how empowering it could be for a speaker to be able to summon his/her audience’s attention.

In this blog post, I am arguing that when one engages in deep listening, or paying attention to the interlocutor with empathy and appreciation, one gives agency to one’s interlocutor by giving them respect.

As human beings, we all want to be treated with respect. It’s an instinctive desire. I had such a low expectation of how my peers would treat me in an English class, thus when I was treated with respect, I felt elated, happy, and empowered. What if we use the same practice for doing research? What if deep listening as a way to show respect to a research subject is a principle in doing qualitative research?

During the Covid-19 Pandemic, everything has been moved online. Now everyone knows what Zoom is, and conducting research interviews has become so much easier than before because teleconferencing has become normalized as a social practice. My argument is that in the 21st century, where more activities are conducted online, sociologists should also conduct their research online. I think that conducting interviews online should become a part of interviewing methods. It should be in one’s research repertoire, in one’s tool box. This research environment is not ideal for a lot of projects. However, as researchers we should make do with what we have.

In the current situation, when researchers interview participants online, we need to practice deep listening, and pay attention to it more than ever. What does it mean? It means letting subjects sufficient time and care to elaborate on their points, and giving them virtual space to feel comfortable. I think psychologists have done this very well when we all transitioned online. Now all psychotherapists are offering online therapy. They use virtual spaces very well. They use virtual platforms to elicit deep emotions, deep connections, and deep openness with their subjects. Sociologists should learn this deep virtual listening practice from them.

What constitutes deep listening then? There are three basic components of virtual deep listening: (1) establishing virtual rapport, (2) maintaining eye contact and attention, (3) asking follow-up questions.

Establishing virtual rapport: Establishing quick rapport to any person is an art. A great field worker often incorporates humor, and the ability to relate to the interlocutor in the first five minutes of interaction with a new interview subject. Establishing a virtual rapport presents a challenge because the interviewer is no longer being physically in the same place as their interviewee. This physical distance creates a challenge because relating to somebody virtually is a very different skill than relating to someone in the same physical space. It seems that podcasters have figured out how to establish quick rapport with their interlocutors very quickly. I would love to know how they do this. What are some tips, and what should one pay attention to?

Maintaining eye contact and attention: this is true when a researcher interview some one physically. It is even more true when an interview is done virtually. Maintaining eye contact via Zoom is very difficult. Sometime we do not know whether we are looking directly at the other person in the eye. This act of staring at a screen for too long might lead to Zoom fatique, the feeling of tiredness, anxiousness or worry with yet another video call. Yet in order to get the best interaction, and that to help the interviewee to come forth with their life stories, paying attention to what being said, and how they say it is utmost important. Paying attention to details is always the best working guideline.

Asking follow-up questions: As I interviewed podcasters for a research on the podcasting industry, I learned that not everyone is a good podcaster because they do not know how to ask follow-up questions. This point relates to the previous point about paying attention to details. I have the habit of taking detailed notes when people talk. It’s a very good practice to get things visually in front of you when you want to know what is being said. Many a times, I used my notes to come back to points that the interviewee said, and I needed more elaboration. A superb fieldworker does not take detailed notes. They only need to take mental notes, and write down very short notes. Then when they go home, they will fill in the blank what is missing from their notes. This is a great mental exercise. I strongly believe that a good fieldworker has very good memory. I am often afraid of losing track of the conversation, thus I take notes of everything. Remember asking follow-up questions for further elaboration is always helpful when the interviewee talks about a social concept using their own words, and to construct their social life through their own lens.

In conclusion, deep listening is an important practice for everyone in this busy world where technology makes us more isolated than ever. For a researcher, deep listening helps us connect with research subjects because it is a way for us to give our interviewees respect. In the context of virtualizing research, deep listening is even more needed because it can help us to bring down the physical distance of a Zoom call. All in all, I would encourage everyone to think about different ways in which deep listening could be practiced, and how it is being applied in different contexts.

Audm vs. Diversity in the Age of #BlackLivesMatter

While America is experiencing a social revolution lead by Black Live Matters activists, every individual, every institution is forced to pay attention to the question of diversity, inequality. At the same time, as I am reading news coming from different sources about Covid19, and social justice, I feel that I appreciate the good work that journalists do. I read about the troubles that journalists of color are going through. Newsrooms across the nation are grappling with the racial inequality conversation that the nation is having. I want to support their work, especially supporting good works by journalists of color.

Then I found Audm, an app that reads high quality news articles aloud. The company has recently acquired by the New York Times. Some observers have said that this acquisition marked a turning point in the New York Times’s approach to audio content, and audio production. The New York Times has beefed up its audio content production. Its the Daily podcast is one of the most popular podcasts in the world. The news organization is now behaving more like a tech company than a newspaper company. Its Data Science department is staffed with some of the most well-known data scientists in the world. Its constant acquisition of startups makes it looks like Amazon, a website of everything. I wonder at what point all of my news source would come from some organization that is associated with the New York Times.

At first, I rejoiced at the idea that now I can listen to the highest news content by a very cool app. It felt authentic, and intimate like listening to a podcast. At the same time you’ll get to know the most important information out there written by the best journalists in the industry. Then after having listened to a few articles, I found one pattern: all of my news is read by white men even when the news was written by a brilliant writer of color, or a female writer of color. This does not sound right to me. Instead of giving power to the writer, curating some of the most important content to readers, the app and its voice over staffs reproduce a type of “audible inequality” in the voice over industry. If there’s diversity of writers among New York Times staffs, I’d want to also have diversity in voice over actors.

Even when second generation Asian Americans born and raised on American soil, they have a distinct voice that is different from a middle age white man’s voice. For example, a Vietnamese young writer would have a voice that has been nourished by their migrant parents who came to the US, overwhelmingly in the aftermath of the Vietnam War. This person has been raised within a community that is still grappling with the idea that they are now a minority group in an increasingly diverse society. The young writer has been nourished with fish sauce, and Pho, and a history of the Vietnam War, and growing up being told a model minority student. The voice that this young individual produces is representative of all of those lived experiences. It is unique and distinct. I want to listen to an article written by a Vietnamese talented journalist, being read by a talented Vietnamese voice over actor. In that process, an intellectual work (the article) benefits two knowledge workers (the writer, and the voiceover actor) of color.

What is happening now is that the writer of color does the difficult work of producing a piece of intellectual work (the article), and it is read by a white middle age actor, who benefits from the first person’s work, and reproducing the stereotype that only white voice actors are talented because their voices are featured. This reality incurs symbolic violence on the talented writer, and reinforcing existing income and racial inequality that upholds the current economic structure. When the audience does not critically think about what they listen, they gradually acquire an association that there’s no talented voice actor of color out there. This is especially damaging for young people of color who would dare not go into a field like voice over because they never saw any people like them in the field.

To conclude, I suggest that Audm, and by extension the New York Times, should diversify its cast of voice actors. If an article is written by a writer of color, it should be read by a voice actor of color. On a broader scale, the audio industry itself should diversify. There are plenty of opportunities for voice actors of color to contribute, they should be given roles and opportunities where appropriate. As of now, I will not subscribe to Audm. I don’t think my money would be well spent here. I would rather read the article written by the talented writers of color, and imagined how they would sound in my head rather than listening to the app, whose voices do not represent the real writers. And then donate money directly to an artist of color on Patreon, where I know for sure that I directly contribute to their creative work.

Attention Economy 101

I am trying to figure out the concept attention economy, its genealogy, and how I can apply it in the contemporary media landscape. My first step was to check Google n-gram to see when the concept most in vogue. Here is what I found:

Screen Shot 2020-06-02 at 6.57.09 PM

This graph basically suggests that the concept was used a lot in Google books between around 1995 to 2013. It was most used around probably 2004. Then the frequency reduced.

The same diagram is rendered a bit differently when I chose to smooth out over the period of one book.

Screen Shot 2020-06-02 at 7.00.13 PM

This figure shows two peaks: possibly 2003, and 2007, after which point the mentioning of the phrase “attention economy” gradually dies down.

Now let turn to Google Trends to see how and whether this phrase shows up

This Google search term mirrors the Google Book results. I think the only difference is that Google trends are search terms floating on the internet, while Google ngram reviewer reflects the term being mentioned in books.

Screen Shot 2020-06-02 at 7.24.57 PM

This figure shows that the term “attention economy” was used a lot between 2004 (the year Google Trends started documenting terms I guess) and 2008. Then the interest in the term died down. The number seems to pick up a bit since 2018 until now, but it does not look significant.

If you compare the term “attention economy” with “attention” only, the result is pretty revealing:

Screen Shot 2020-06-02 at 7.29.01 PM

The red line represents the search result for “attention,” and the barely recognizable blue line represents result for “attention economy.” The overall trend for “attention” seems to go up a bit, while the trend for “attention economy” is almost zero. This is weird. I wonder why people no longer use the term “attention economy.” Does it mean it is out of vogue? Sometimes a term is defined, and then being criticized for not being able to capture a certain phenomenon, then it completely disappears from our linguistic circulation.

According to Wikipedia:

Attention economics is an approach to the management of information that treats human attention as a scarce commodity, and applies economic theory to solve various information management problems. Put simply by Matthew Crawford, “Attention is a resource—a person has only so much of it.”

This concept should be understood within the context of the digital economy and the information economy because only when there is a flood of information that attention becomes a rare commodity. And since information is so cheap to come by, companies such as Facebook, Twitter, and other platform companies are designing software, and platforms intentionally with the idea that attention is rare, and that they should design interfaces that capture the most attention (measured by how many minutes or seconds a persons spend scrolling on their platforms).

This concept clearly came from economics, and being applied to different economics-related fields such as marketing, management, user research. As a sociologist, I need to ask, so how are social relations formed, sustained, and reproduced in this economy? What are some characteristics or attributes of this economy should I pay attention to? Is an attention economy on Youtube different from an attention economy on Tiktok, on Twitch, and other platforms. How does it work differently on Instagram than on Twitter? What are some advantages and disadvantages of this economy to content creators, and their audience? How does this concept illuminate the podcasting phenomenon that I am examining?

There are lots to be said, and examined here. I am excited about the concept, and I am looking forward to learning more about it, and how to use it in my future work.

Life After a Manuscript Submission: Freeing Mental Space

Last week I submitted in a blog post manuscript for a center that I’ll be affiliated with in the next two years. Once the blog post was submitted, I felt very good about myself. I felt that I could start investing my time on something else such as writing posts for my own blog. During the writing process, I imagined what my life after the submission of the manuscript could look like. I dreamed that I could spend more time watching Netflix. I imagined that I would be more productive writing my personal reflections. I imagined that I would spend more time reading, and writing for other “important” research. I imagined publishing research articles.

Now the manuscript was submitted, I felt a sense of relief, but I still have not picked up anything that I thought I would do yet. I have another blog post to write for a well known public-facing blog in my discipline. I am slowly but cheerfully moving on to the next publication projects. My mentor once made a remark that writing momentum is what I am looking for. Once I get in the flow, I would be able to produce writing regularly. Every publication is in and of itself a project that takes a lot of brainstorming, writing, editing, and revising work. However, I feel like I have figured out the process, and that I am onto the next big thing in my life after each piece is turned in, getting comments, and suggestions from editors. I think I am gradually getting into this publication flow.

Once a manuscript is submitted, I feel confident about my ability to write, and that I have things to say. My mind is looking for the next challenge that I should engage in. Today, I emailed another editor about a new manuscript that will be due by the end of the month. They responded immediately. They were responsive probably because I have submitted a manuscript to them before. Now I am stacking projects on my plate. “One project in, another project out” is my current modus operandi.

I figure out that my work flow for each writing project includes (1) coming up with an idea (2) figuring out a theoretical framework (3) collecting thoughts, evidence, documents, arguments, (4) talking to friends, colleagues about my ideas, and the direction of the essay (5) coming up with counter-arguments to see how I can improve my writing even further.

For example, for the blog post that I am hoping to send out by the end of this week, I am still collecting data. I have written at least half of the post to figure out what I am thinking. I really practice the idea “I am writing myself into knowing.” Having the first draft done is always the most challenging. Once it is done, I can strengthen it by adding or dropping certain arguments, and/or evidence. My essay is half done now, and I feel good about the progress at least. My goal for today is to dust off the first draft, take a look at it, develop it a bit more, and send it out to get immediate feedback from my writing partners.

In the process of writing the above-mentioned piece, I recognize that I am not yet a fast writer. I am not yet at the level where I can produce an op-ed for a newspaper in less than a week. My writing often takes somewhere between two weeks to a month. Once I submit these hypothetical manuscripts, they are no longer topical. The world has moved on to new issues, new social phenomena. The writing process also takes a lot of emotional and mental energies. It is exhausting to write about current events as well because we’re still living them. Our minds are still trying to figure out what is the meaning of what has just happened. Sometimes, I feel being distanced from an event might help with comprehending it. Yet, if I give myself time to think about an event, and write about it, maybe I’ll understand it a bit more, and I will also help other people understand it through my writing.

As writers, scholars have words to express their thoughts and arguments to the world. However, being too slow of a writer might hurt their chance of having their ideas heard because if they are too slow, the world has moved on from the issue that they write about. Timeliness is key in writing as well as in other areas of life. It’s a misconception that academics and intellectuals have all time in their lives to think about the world, and carefully craft each sentence. Writing has a lot of hidden pressure, and anxiety. In the digital age, producing timely work is more important than ever.

Following are a few guidelines about writing for contemporary society:

  1. Timeliness: Producing good work, solid work, but the speed at which one produces should be quick. The news cycle in our contemporary society has become so fast. If a scholar does not address an important issue, they might be working at the margin of society, and that their ideas would never become relevant.
  2. Being relevant: Addressing issues that are relevant to different communities of audience is an important skill. Scholars often communicate with different audiences. Figuring out what issues are relevant to which community is an important first step.
  3. Framing the issue in a theoretical way: Attaching contemporary issues to bigger sociological debates is a trick that sociologists do in order to make sure that contemporary issues speak to timeless theoretical debates. This is a skill that graduate students like myself take a long time to learn. We’re still figuring out what the theoretical debates are. In order to relate a contemporary event to a theoretical debate, and write about it in an intelligent way, one needs to practice, and think a lot.
  4. Solid research: Before writing anything, one needs to gather evidence, and do solid research. Opinions without facts are useless.
  5. Jargon-free communications: Graduate students tend to synthesize other people’s ideas a lot to show that they are well-read, and that they understand dense social theory books. Yet in order to make a theoretical idea digestible to the mass public, one ought to know how to convey that idea without using sociological jargons. This is also a very difficult skill to learn. It takes patience and lots of practice to master.
  6. Feedback: As in any creative project, getting immediate feedback from trusted friends and colleagues is very important. Feedback is gold in the publication game.
  7. Develop a working relationship with journal editors: if one has a working relationship with editors, they would be more welcoming one’s next ideas, and next projects. Thus developing a solid working relationship with journal editors is very important. At the end of the day, academia is a reputation-centered economy. One has to develop one’s own reputation, and that one’s reputation is also judged by others. Reputation is currency in a knowledge economy.
  8. Submit and move on to the next piece: Having the next piece in the pipeline is very important. Once a piece of writing is submitted, the author should start another project immediately. This is to keep the momentum going. I cannot emphasize enough the importance of a publication cycle, and that the more one writes, the more one would be inspired to write more.

“Ideas beget ideas.” This idea never gets old. Whenever I get a publication out of the door, I feel happy about myself, and I feel inspired to write the next piece. Maybe one day, one of my pieces would become influential. Maybe one piece would become viral. Maybe my writings would change someone’s mind, and have some policy implications. As of now, those are far-fetched. My only writing goal now is to produce consistent work regularly. I prefer the productivity model at this point. At some point in the future, this productivity model might turn into a high-impact model. As I am still learning the ropes of publishing, the productivity model is most relevant.

Price of Living in a Poor Neighborhood

New York City is a strange place. The housing market is extremely tough for young families, and newcomers. Today I was curious to see how my neighborhood, Central Harlem, which is considered to be a poorer neighborhood than many other neighborhoods in the city. I googled to see the stats of the neighborhood with a specific emphasis on the housing market. Its median household income is $47,708 about two-thirds of the amount in New York, $67,844, and about two-thirds of the amount in United States: $61,937. In other words, I am living in a less wealthy neighborhood of the city. Its poverty ratio is 25.1%.the rate in New York: 13.6%, and nearly double the rate in United States: 13.1%.  My neighborhood is also poorer than New York City in general, and poorer than the United States.

However, when I look at housing value in my neighborhood. This is what I found:

Screen Shot 2020-05-31 at 3.11.37 PM

If a person owns a house at all, 50% of the houses have values of $671,000 or more. That’s a lot of money. It’s more than double the amount in New York, and more than double the amount in United States.

Here’s the further breakdown of the housing values in Central Harlem:

Screen Shot 2020-05-31 at 5.31.08 PM

I have always known that housing is expensive in New York, but when I look at housing values in my neighborhood, I was actually very surprised. 35% of owner-occupied housing units valued at 500,000 to 1 million US dollars. This is an exorbitant amount. 15% of the owner-occupied units are valued over 1 million US dollars. If I was to buy a housing unit (an apartment, or a house) in Harlem, and if I was a college professor at one of the local colleges with salary around 100K a year, given that I could save about 20% of my salary to buy a housing unit, it would take me at least 25 years to pay for a $500,000 apartment.

This calculation makes me realize two things. One, I do not think a college professor can buy a good house in Harlem. Maybe I have chosen a wrong career for financial stability. Two, if I want to remain in academia, I will join an exodus of New York emigrants to the suburb where housing is a lot more affordable than Manhattan. Manhattan squeezes out the middle class families.

At the end of the day, it’s such a contradictory existence. While my neighborhood is on paper poorer in terms of wealth, and income than other neighborhoods in New York City, its housing value is twice that of the city average. How are my neighbors getting by with  this precarious financial existence? What are some psychological and social impacts of both poverty on the lower end of the socio-economic spectrum, and housing pressure on the middle class, and upper end of the income ladder?

I have been living in this neighborhood for almost four years now. As a sociologist, I am immersing myself in the neighborhood experience, and getting to know “public characters” of the neighborhood in different aspects of life. The term public characters in Jane Jacobs’s definition are “often people like storekeepers, barkeepers, and pastors … By thinking, talking, and interacting out in the open, these people create our public life” (Eghbal). I am currently following the new public characters in the digital era in the New Harlem. My public characters are business owners, and tech founders. I would argue that in the new economy, business owners, and tech founders are creating a new kind of culture, and creating new kind of public life. This public life though is privatized, and operating around commerce.

I love this neighborhood, and feel that I have things to contribute. However, at this point, I am still not sure how I would be able to contribute to the enrichment of people from Harlem. So far, I have been struggling with the idea that I’m a gentrifier who is displacing somebody that I never got to know, and that I’m contributing to future waves of gentrification because with me, there are new restaurants and commercial places.

In a nutshell, living in Harlem is full of surprises on the cultural fronts, but on the economic fronts, the neighborhood is structurally difficult for everyone.

Principle Component Analysis

Today my goal was to figure out principle component analysis in R. This is something that I have been trying to understand for quite a while now. This takes some time to conceptual understand what PCA does. My RStudio is still arrested in an experiment. I wonder what kind of project I can use PCA for. Unlike the experiment I did yesterday with visualizing a text corpus in R, the experiment today is computationally expensive, and conceptually confusing. At the end of the day, I feel that I have not accomplished anything meaningful, and that my ability to use machine learning has not increased a single bit.

This summer I will be attending the ICPSR summer institute to study Machine Learning for Social Science Research. One of the lectures has to do with how social scientists have used PCA in their research. I need to ask the professor how I can use it in my own research. I need more examples in order to fully understand how it is used.

Figuring this particular machine learning module is both conceptually and technically challenging at this point. I wonder what kind of dataset should I use in order for this particular technique to be useful.

Sometimes, it takes a well-designed exercise for me to fully grasp what the particular technique does, and how I can use it in my future work.

WHAT DOES IT ALL MEAN I SO CONFUSE - Jackie Chan Meme - quickmeme

Visualizing Data as Discovery

I have been obsessed with data visualization lately. My go-to tool at this point is till R, which I have been told over and over again that it’s not as versatile as Python. However, it’s the matter of path dependence, and that I am used to figuring out how to to ask the right questions in R in order to get desirable results.

With Python, different steps that I need to figure out to get desirable results are still black boxes to me. While writing this blog post, I have realized that I really need to master the Python programming language this summer. I have gotten down the basics. It is the matter of practice. Thus, this is the right time for me to actually sit down, and become really familiar with python, and be able to produce work using python programing language.

Back to the issue of data visualization. Today, I spent 8 hours straight trying to figure out how to create stacked chart in R. I have been trying to create it for quite a while. It started about 4 weeks ago when I promised my research partner that I would create a stacked chart figure for our text mining paper. I asked all people I know around to help me. They all did not deliver. Today, it turned out, I rolled up my sleeves, sat down in front of my laptop, and figured how to create the chart. My final result is not as clean as what I would have liked. It’s nowhere close to a scientific journal level quality. But the figure conveys the main idea, and that it is sufficient for me to draw some conclusions from the data.

This is the figure that I produced after a day trying to create it. Besides, it also took some serious conceptual understanding of what this figure actually represents. In other words, I learned both the technical skills, and the conceptual understanding behind the process of creating it. legend18

What I did was that I downloaded a corpus of text from the subreddit podcasting, a community dedicated to creating podcasts. My goal was to create a stacked chart that demonstrates different topics over time. The topics are represented by trigrams. Specifically, I calculated top trigrams per month, and charted them over time. Even though I downloaded all content from the subreddit, which started in 2010, I found that trigram chart only matters once I narrowed down the date range to 2016-2020.

The resulting figure shows that the subreddit started with accepting promotional podcasts, then became dominated with weekly podcast discussions, and technical discussions (such as mic, mixer audio interface). One topic that remains central over time is the different podcast distribution platforms (Apple Podcast, Google, Spotify).

The overall topics concern with technical aspects of producing content, and the different main platforms that one could distribute episodes, as well as finding shows. From these various topics one can conclude that in the past 5 years of the previous decade, the podcasting community focused a lot on the technological aspects of the field.  Technology matters from both sides: creation and consumption. Thus, it seems that the main driver of the podcasting field so far has been the sheer development in technology both for content creation and content consumption. What is surprising to me through this exercise is that the discussion about how to monetize a podcast doesn’t show up at all  in the top trigrams per  month analysis. This raises a question about whether the goal of being able to make money from producing a podcast ever a goal for a podcaster.

After spending my weekend working on this little project, I actually felt good about my product. I felt that I actually spent a day building something, and that at the end of the day, I actually saw the result of what I built. This satisfying feeling made me recognize how much I actually appreciate coding. One computer scientist I followed wrote in his newsletter that code doesn’t lie. One knows the exact effects of all the actions. When the final results are not attained, no way bullshitting would help.

More visualizations will come out of my work in the next weeks to come. So far, I am very happy with my progress in learning data visualizations. The more I get into visualizing data, the more I understand the importance of being able to use charts and graphs to understand the social world that we’re living in.

Students’ Reflections

When students submitted their final exams, some sent me notes saying that they had a productive semester together with me despite bumps and also Covid19!. Following are a few that I exceptionally appreciate

“Your class was one of the more useful ones I’ve taken in my master’s program.”

“Thank you so much prof for all your patience, communication and feedback really made this semester very smoothly.”

These feedback really made my heart smile! Teaching has gradually become more enjoyable to me because I can now motivate students my students to give more to the class, and to their final projects.

Community Gardens in NYC

This week, I attended a presentation about community gardens in New York City. From a sociological point of view, it turns out that community gardens can be considered an extremely interesting social phenomena. For example, community gardens could be points of ethnic and racial conflicts (see research by Sofya Aptekar). During the talk, I figured out that there are two community gardens on my block. This is wild. I have walked by them many times, and have seen people hanging out in them. But I have never walked in the gardens, or started planting any trees. And my digging into the community garden data available on NYC OpenData reveals extremely interesting patterns.
6clusters
The dataset provides 536 data points for data analysis. This is indeed an incomplete dataset because I learned that there should be about 600 community gardens in New York City.
Then I did a k-means clustering analysis to create different clusters. I simply went on Github  and got some off-the-shelf python code that do spatial clustering.  Then I altered the code such that it works with my computer, and my data. After trying 3, 4,5 and 6 models, I concluded that 6 clusters look like a reasonable model for this dataset. The above figure is the final result of the cluster analysis. One can do more fancy GIS visualizations. I think that this figure is already very telling about spatial clustering of community gardens in New York City.
The common wisdom is that community gardens are located in the low-land value areas.  This figure certainly supports the sentiment, but it also shows a more complicated picture. The dense clustering areas are black dots or those community gardens in the Bronx. Then there is a dense area in between Brooklyn and Queens. The rests are pretty sparse. Looking at the map, one sees that Upper Manhattan is grouped with the Bronx (black dots), some parts of Queens, Brooklyn, and lower Manhattan, and the Upper West Side are grouped together. Then the middle part of Queens is its own cluster. And outer part of Queens is another cluster. Staten Island is another world in and of it self.
I showed this map immediately to a colleague and she confirmed that the map captures what the literature says. The literature says that community gardens are mostly located in low land-value areas, relatively low land values, with ethnic and racial minority populations, and all on city-owned land that the city took control of because the building owner did not pay property taxes for three years.
So that’s my attempt at getting a handle of k-means clustering. I’ll be working more on k-means clustering for other projects. The more I use this technique, the more I think that there are lot of ways that I can use it in different projects. Over the summer, I’ll improve my python skills to write my own k-means clustering code. As of now, I am happy learning and working with other people’s codes.