Archive for the ‘social construction’ Category
This month, we are reviewing Catherine Turco’s Conversational firm. Earlier, I summarized the contents. The book is an ethnographic account of a tech firm that uses social media for internal communication. Turco’s main goal is to advance the argument that social media has substantially altered communications and hierarchy inside firms. Now, I’ll highlight some strong points of the book and next week I will raise critiques.
First, the book correctly points out that the interactional order of firms is now quite different in the social media age than before. In a world of paper based communication and face to face meetings, it was relatively easy to control who knew what. In contrast, it is now possible for modern firms to have much more wide ranging discussions. The project manager really does have (some) direct access to the CEO. This is truly remarkable.
Second, the book discusses the possibility that authority may be redefined in this situation. If everyone at work has a wiki where they can discuss the firm’s issues, then managers may end up giving away power to others.
For me, these two lessons point to an important issue in organizational design – the importance of social media as a tool for “flattening out” the organization. This has gotten a lot of attention among business writers and management scholars. The lesson I take from Turco’s book is that the story is complex. On the one hand, yes, social media democratizes the culture of many firms. But on the other hand, this is not straightforward or even desirable in many cases. The “internal” public sphere of a firm may not be the best place to settle policy. By allowing the middle of the organization to define issues, it may or may not be valuable or constructive.
Next week: Why didn’t Turco talk about laziness?
Former guest blogger Mito Akiyoshi has a new article in PLoS One about perceptions of fairness in the family. From the abstract:
Married women often undertake a larger share of housework in many countries and yet they do not always perceive the inequitable division of household labor to be “unfair.” Several theories have been proposed to explain the pervasive perception of fairness that is incongruent with the observed inequity in household tasks. These theories include 1) economic resource theory, 2) time constraint theory, 3) gender value theory, and 4) relative deprivation theory. This paper re-examines these theories with newly available data collected on Japanese married women in 2014 in order to achieve a new understanding of the gendered nature of housework. It finds that social comparison with others is a key mechanism that explains women’s perception of fairness. The finding is compatible with relative deprivation theory. In addition to confirming the validity of the theory of relative deprivation, it further uncovers that a woman’s reference groups tend to be people with similar life circumstances rather than non-specific others. The perceived fairness is also found to contribute to the sense of overall happiness. The significant contribution of this paper is to explicate how this seeming contradiction of inequity in the division of housework and the perception of fairness endures.
Nice application of reference group theory. Once again, more evidence that happiness and grievance don’t always reflect material conditions.
asian american privilege? a skeptical, but nuanced, view, and a call for more research – a guest post by raj ghoshal and diana pan
Raj Andrew Ghoshal is an assistant professor of sociology at Goucher College and Yung-yi Diana Pan is an assistant professor of sociology at Brooklyn College. This guest post is a discussion of Asian Americans and their status in American society.
As a guest post last month noted, Asian Americans enjoy higher average incomes than whites in the United States. We were critical of much in that post, but believe it raises an under-examined question: Where do Asian Americans stand in the US racial system? In this post, we argue that claims of Asian American privilege are premature, and that Asian Americans’ standing raises interesting questions about the nature of race systems.
We distinguish two dimensions of racial stratification: (1) a more formal, mainly economic hierarchy, and (2) a system of social inclusion/exclusion. This is a line of argument developed by various scholars under different names, and in some ways parallels claims that racial sterotypes concern both warmth and competence. We see Asian Americans as still behind in the more informal system of inclusion/exclusion, while close (but not equal) to whites in the formal hierarchy. Here’s why.
Earlier this week, I suggested a lot is to be gained by using computational techniques to measure and analyze qualitative materials, such as ethnographic field notes. The intuition is simple. Qualitative research uses, or produces, a lot of text. Normally, we have to rely on the judgment of the researcher. But now, we have tools that can help us measure and sort the materials, so that we have a firmer basis on which to make claims about what our research does and does not say.
The comments raised a few issues. For example, Neal Caren wrote:
An important frontier in sociology is computational ethnography – the application of textual analysis, topic modelling, and related techniques to the data generated through ethnographic observation (e.g., field notes and interview transcripts). I got this idea when I saw a really great post-doc present a paper at ASA where historical materials were analyzed using topic modelling techniques, such as LDA.
Let me motivate this with a simple example. Let’s say I am a school ethnographer and I make a claim about how pupils perceive teachers. Typically, the ethnographer would offer an example from his or her field notes that illustrates the perceptions of the teacher. Then, someone would ask, “is this a typical observation?” and then the ethnographer would say, “yes, trust me.”
We no longer have to do that. Since ethnographers produce text, one can use topic models to map out themes or words that tend to appear in field notes and interview transcripts. Then, all block quotes from fields notes and transcripts can be compared to the entire corpus produced during field work. Not only would it attest to the commonality of a topic, but also how it is embedded in a larger network of discourse and meaning.
Cultural sociology, the future is here.
Last week, I argued that many sociologists make a strong argument. Not only are social classifications of race a convention, but there is no meaningful clustering of people that can be derived from physical or biological traits. To make this claim, I suggested that one would need to have a discussion of what meaningful traits would include, get a huge sample people, and then see if there are indeed clusters. The purpose of Shaio et al (2012) is to claim that when someone conducts such an exercise, there is some clustering.
Before I offer my own view of the evidence that Shiao et al offer, we need to set some ground rules. What are the logical possible outcomes of such an exercise?
- The null hypothesis: your clustering methods yield no clusters (e.g., there are no detectable sub-groups of people).
- The weak hypothesis: clustering algorithms yield ambiguous results. It’s like getting in regression analysis a small correlation with a p=.07. This is important because it should shift your prior moderately.
- The “conventional” strong hypothesis: unambiguous groups that correspond to social classifications of people. E.g., there really is a “White” group of people corresponding to people from Europe.
- The “unconventional” strong hypothesis: unambiguous groups that do not correspond to common social classifications of people. For example, there might be an extremely well defined group of people that combines Hawaiians and Albanians.
A few technical points, which are important. First, any such exercise will need top incorporate robustness checks because clustering methods require the use to set up initial parameters. Clustering algorithms do not tell you how many groups there are. Instead, they answer the question of how well the model fits the hypothesis that you have X groups. Second, sociologists tend to mix up these possible outcomes. They correctly point out that there is a social construction called “race” which is real in its effects and influence on people. But that doesn’t logically entail anything about the presence or absence of human populations that are differentiated due to random variation of inherent physical traits over time. Also, they fail to consider #4. Their might be actual differences, but they might not match up to our common beliefs.
So what does Shiao at al offer and where does it lie in this spectrum of possibilities? Well, the article is a not a systematic review of genomic research that searches for clusters or people. Rather, it offers a few important points drawn from anthropology and genomics. First, Shiao et al point out that there is a now undisputed (among academics) human history. Humans originated in East Africa and then spread out (“Out of Africa thesis”). Second, as people spread out, genomic variation emerges as people mate with people close by. Third, genetic drift implies that geography will predict variations in genes. As you move from X to Y, you will see measurable differences in people. Fourth, these differences are gradual in character.
Shiao then switch gears and talk about clustering of people using genomic data. They tell us that there are statistically detectable and stable group differences and that these do not rigidly determine behavior. They also cite research suggesting these statistical groups correlate with self-described racial groupings. Then, the authors discuss a “bounded” approach to social theory where biology imposes some constraints on the variation on behavior but in a non-deterministic fashion.
I’ll get to the symposium next week, but here’s my response: 1. There is a real tension. At some points, Shiao et al suggests a world of gradual variation, which suggests no distinct racial groups (outcome #1) but then there’s a big focus clusters. 2. If we do live in a world of gradual, but real, variation in human biology, then the whole clustering approach is misleading. Instead, we might live in a world that’s like a contour map. It’s all connected, there are no groups, but you see some variables increase as you move along the map. 3. If that’s true, we need an outcome #5 – “race is not real but biology is real.” 4. I definitely need more detail on the clustering methods and procedures. Some critics have pointed out that the clusters found in research are endogenously produced, which makes me suspect that the underlying science might be hovering around outcomes #1 (it all depends on the algorithm and its parameters) or #2 (there might be some clustering, but it is very poorly defined).
Consider the following approaches to the same in issue – fetal alcohol syndrome (FAS). In 2000, Elizabeth Armstrong and Ernest Abel published an article in the journal Alcohol and Alcoholism arguing that fetal alcohol syndrome had become a moral panic. Even though people had become obsessed with FAS, there was actually very little evidence to suggest that moderate alcohol consumption damaged fetuses. This argument is elaborated in the 2008 book Conceiving Risk, Bearing Responsibility. In 2013, the economist Emily Oster published a book called Expecting Better, which assesses pregnancy advice with a review of the pertinent clinical evidence. Like Armstrong, Oster finds that the norm against moderate alcohol consumption is not supported by the data.
The comparison between Oster and Armstrong is revealing. For example, more people know about Expecting Better because, frankly, economists are more respected than sociologists. But there is a deeper lesson. When Oster frames her work, she presents it as a morally neutral project. Her framing is roughly: “Statistics is hard, people may not have all the facts, and you might have a mistaken belief, but as an economist, I am trained in statistics. I can help you make a better choice.” Thus, the reader is morally blameless.
In contrast, Armstrong’s approach to FAS relies on standard explanations of moral panics in sociology. It goes something like this: “The facts we believe reflect our underlying biases. These biases reflect our evaluations of certain types of people, who may not deserve that stigma.” Thus, if the reader buys FAS, they are implicated in an immoral action – unfairly exercising gender prejudice. Heck, all of society is implicated.
This is an interesting observation about the public image of disciplines. Economists may advocate unpopular policies (e.g., they are often critical of minimum wage laws) but their moral framework is fairly neutral and technocratic. If you don’t buy my policy, it’s probably because you aren’t aware of all the factors involved. You haven’t calculate the social welfare function properly! In contrast, sociologists often make arguments that implicate the moral character of the audience. And that doesn’t buy you a lot of friends.