Digital sociology project summary

Over the course of the semester, I have been exploring the topic of race and genetics research. The first blog post (In brief) examined the role of expertise and how intersectional factors may influence who is considered an expert. While I intended this post to somehow lead towards validating the scientists when interpreting genetic findings, my research veered away from the scientists and analyzed the scientific basis for claiming genetic differences between groups of people.

As a result, the second blog post (In pictures) looks at some of the available images online obtained through a simple keyword search, which have been used by various individuals on blogs or discussion forums as evidence either for the biological nature of racial differences or the socioeconomic disadvantages/cultural construction of race.  This post also engaged some of the sociological literature around the problem of valuing problematic scientific assertions over the social, historical, and cultural context for defining race.

The third post (In trends) showed several statistical images and graphics supporting this narrative as well as additional sociological literature. Scientists have illustrated that a close correlation exists between genes and geography. However, Europe’s geography has a long history that likely contextualizes these genetic clusters and coincides with racial/ethnic classifications. This kind of discovery provides support for the viability of using genetic testing for ancestry, but also presents a significant problem for defining racial/ethnic groups in time. For example, when is a person’s heritage identified as South African versus Dutch (the Boer colonists in South Africa during the late seventeenth century)? The modern answer seems to depend on their physical appearance: white = Dutch and black = South Africa.  Despite these issues, the consumer market for genetic testing has boomed and is expected to reach $340 million by 2022.

Finally, the latest blog post (Culture) views the consumer genetic testing market for its potential benefits and inherent problems. These at-home test kits have wide appeal to millions of curious consumers, but contain black-boxed science and likely problematic methodologies.

Why does this topic matter? Science has a cache for being empirical and objective. The popularity of the at-home genetic testing kits sold to millions of consumers reinforces the bad notion that race is determinable from our genes, and therefore biological, rather than socially constructed. The social construction of race actually forms the fundamental basis for scientific categorization, but scientists get caught up in their own scientific claims or a normal person can’t get through the technical nature of scientific writing enough to decipher  this message. Alternatively, a company may be motivated by profit to make the science sound much more certain. The direct-to-consumer genetic testing industry is growing, which also indicates that the belief in the biological construction of race might grow too.

Culture: genetics testing and uncertainties

Genetics research revolutionized the modern world through its various applications on genetically modified crops, cloning sheep, and gene editing for embryos. These topics caused controversies over tampering with the natural order and ethical considerations about setting boundaries for scientific research at a societal level. However, various companies offer direct-to-consumer products relevant on a more individual level. These genetic testing kits promote a healthier life though knowledge about your risks for certain diseases or more effective drug treatments. For example, BRCA gene mutations increase a woman’s risk of breast or ovarian cancers. In 2013, the Food and Drug Administration issued warning on companies marketing their products for diagnosing and treating medical conditions without sufficient evidence of accuracy for these purposes.

However, the direct-to-consumer genetic testing market remains robust. The Guardian recently cited the lucrative market for consumer genetic testing at an estimated $70 million in 2015. MIT Technology Review reported that AncestryDNA and 23andMe each announced their millionth consumer sample in 2015. One recent article quoted a nonprofit advocate that made an analogy between a woman learning she is pregnant through a test commonly available on a drugstore shelf and where personal genetic testing kits will be in ten years.

The average consumer cost for these genetic testing kits ranges widely, but the U.S. National Library of Medicine states the average cost for at-home genetic testing ranges from several hundred dollars to more than a thousand dollars. Another informational website estimates the cost as $69 to $1,399. The cheaper tests provide basic genealogical data, but the more expensive tests might examine multiple chromosomal inheritances (showing the paternal line, the maternal line, or from both parents). The higher end tests also indicate health risks based on disease markers. For example, 23andMe offers their ancestry service for $99 and their health plus ancestry service for $199.

Why have these at-home genetic testing been popular with consumers? AncestryDNA advertises that genealogical tests will help discover “who your ancestors were and where they came from” with tantalizing potential results “from discovering their ethnicity to connecting with distant relatives.” This might appeal to adopted individuals that reach a dead end in their search for their birth family. This also appeals to individuals that cannot use official records to research their familial origins, such African-Americans. Based on research for her recent book, The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome, Alondra Nelson illustrates how many African-Americans have been curious about their ancestors from Africa and genetic testing offers a scientific method to get answers. However, the genealogical results have done more than provide a sense of ethnic identity. The information also becomes relevant for political uses, such as proving lineage in legal cases to seek reparations for slavery or seeking dual citizenship in African countries.

If we assume that the companies cannot be held responsible for what consumers do with their genetic information, then what other concerns exist about the widespread availability of these tests? First, the cost provides a possible opportunity for socioeconomic inequality, especially when companies advertise the benefits to learning about health-related risks. This places a value on foreknowledge about genetic predispositions or likelihood for certain diseases.

The test results also have the potential for unsettling or unexpected results. For example, white supremacists use genetic testing to brag about their “pure blood,” but sometimes get “bad news.” Researchers studied an online white nationalist community and found the two most common responses: rejecting the test’s validity or rationalizing the result as statistical error. While it might be simple to dismiss these reactions, direct-to-consumer genetic testing is not foolproof science.

The companies offering at-home testing kits have a profit-seeking motive for their products. As a result, they each claim their algorithms and sample populations as intellectual property. Independent scientific experts cannot assess the validity and reliability for these proprietary methods. In addition, the original genetic “admixture” research has fundamental flaws that do not get communicated to the consumer. The Human Genome Project found that all human beings share 99.9% of their genes. In other words, all genetic differences come from 0.1% of the human genome. 85% of the variation occurs within geographically distinct groups and only 15% occurs between these groups. Therefore, genetic testing claims to make statistically significant genealogical distinctions from 15% out of the 0.1%. In addition, no one has “pure” samples for reference from past populations. Companies use “reference samples” that come from modern populations with untestable sample validity. For example, 23andMe claims to “include genomes from 10,418 people who were carefully chosen to reflect populations that existed before transcontinental travel and migration were common (at least 500 years ago).” Are 10,418 people sufficient to account for each Native American nation or African tribe?

Troy Duster, former President of the American Sociological Association, raised his own concerns over genetic science and the implications for race. Genetic science casts scientific legitimacy around the idea that race has a biological basis. Duster (2015) firmly argues that race is socially constructed and social scientists need to reengage in debates about race. When carefully examined, the racial categories used by scientists in genetic research have fundamental assumptions based on social, historical, and folk knowledge. For example, Germany did not exist as a single nation until 1871. If a genetic test goes back 500 years, then would it actually identify someone’s ancestors as specifically Prussian or Bavarian? 23andMe states that “most country-level populations overlap to some degree” and “some genetic ancestries are inherently difficult to tell apart because the people in those regions mixed throughout history or have shared history.” However, an ancestry composition report provides a neat chart with percentages adding up to 100%.

Disclaimer: I received a testing kit as a gift in 2013. My ancestry results seem consistent with expectations, although the specific percentages of variation is surprising.

A final issue to mention about direct-to-consumer genetic testing kits is their fine print. Many people simply don’t read the fine print when signing up for a service or providing consent. Companies make a higher percentage of their profits through the accumulation and sale of genetic datasets for medical research than through sales of the test kits. While the companies claim to anonymize and aggregate the data, researchers found that it is possible to identify men through their genome.

Although consumer genetic testing has promise for improving our health and discovering more about our ancestral heritage, the cons currently seem to outweigh the pros in purchasing a kit. One woman bought a test for fun and found a genetic mystery leading to a discovery about babies switched at birth. My key takeaways regarding these tests would be 1) beware of opening Pandora’s box and 2) interpret your results with a grain of salt.


Social network analysis for family

While social network analysis has been useful in examining complex relationships between large institutions (such as universities) or ideas (such as predicting disruptive technologies), this week’s discussion focuses on more personal matters. In Connected, Christakis argued our social networks influenced happiness or weight. Lois (2016) identified four types of egocentric networks through cluster analysis to predict when couples became parents. From prior research, the four factors supporting the influence of social networks on fertility include: 1) social pressure, 2) social support, 3) emotional contagion, and 4) social learning. His research question is whether his four types of networks identified through have predictive validity.

Lois examined six years of data (in waves) from a sample spanning three birth cohorts, narrowed down to 3,104 respondents between ages 20-42 that initially had no children from the German Family Panel Study dataset. Out of this sample, 332 respondents (the egocentric nodes in the networks) became pregnant or had children. Each of these individuals responded to questions to generate names about their social network (other nodes) and then interpret their relationships (forming edges in the network).

He found four types of clusters within the network: family-remote (small, homogenous friend network), polarized (large, heterogeneous friends and family network), disintegrated (small network without many connections), and family-centered (strong connections with family). The results show the most significance in social mechanisms (the four factors supporting the influence of social networks mentioned earlier) between the family-remote and family-centered types. However, the author notes a significant limitation as the effect (having children) may be caused by other factors (such as age) than networks. Social network analysis helped reinforce the idea that our network can influence life choices, particularly in the area of starting a family.

In addition, I also would argue that this study applies the most to Germans. For example, the following chart shows the percentages of household types:

In comparison:

The differences may be statistically significant and cause different kinds of clusters to be seen in networks that likely would affect the analysis using information from another country.

In contrast, I chose to look at an second article on the other end of the spectrum. Burholt and Dobbs (2014) studied the support networks for elderly individuals in multigenerational/extended households to identify network typologies. The authors believed that the presence of other family might skew researchers’ ability to using network typologies to estimate levels of wellbeing in elderly individuals, so they wanted to identify if these multigenerational/extended households resulted in different types of networks as their primary research question. Their secondary question asked whether these typologies had the ability to predict outcomes (wellbeing or loneliness/isolation).

The authors collected a total sample of 590 older individuals (half male/half female) from the Families and Migration: Older People from South Asia project. This project had collected data using eight questions to support classification by the Wenger Support Network Typology: local family-dependent network, locally integrated network (local family and community involvement), local self-contained network (household centered), wider community-focused network (no local family and more community involvement), and private restricted network (no local family and few other connections). The elderly individual’s family and friends became the nodes with the various supportive relationships (from selected questions about chatting or helping with laundry) as the links.

Using cluster analysis, the authors selected a four cluster result as the most clear and interpretable: 28% multigenerational households: older integrated networks, 27% multigenerational households: younger family networks (largest type of household and more family-focused), ~27% family and friends integrated networks, and 18% restricted non-kin networks. When the authors compared the Wenger types with their new types, they found that significantly more individuals fell within their “restricted non-kin networks” (18%) than the private restricted network (4%). As a result, they concluded that their new typologies better identified individuals who might receive formal services because they lack other forms of support. Social network analysis helped identify a larger vulnerable population than expected because the restricted non-kin networks might decline family assistance and be more willing to pay for outside services.



Technologists look for “disruptive technologies” or something that dramatically causes a paradigm shift. For example, smartphones have largely replaced “traditional” cell phones (chart below is older and stops in 2011). Smartphones can be considered “disruptive” because they enabled widespread mobile internet use, social media, and video calls (see second chart below), which was difficult to use or nonexistent for traditional cell phones.

A. Momeni and K. Rost (2015) examined trends to predict potential disruptive technologies in the photovoltaic industry (VCU library proxy link to article). The research question: can patent-development paths, k-core analysis and topic modeling be used to better predict which technologies might become the next disruptive technologies?  Other previous methodologies had significant limitations for forecasting technological change.

The authors collected patent data from the European Patent Office (EPO) World- wide Patent Statistical Database between 1978-2012.  They used a keyword search for “photovoltaic” and “solar cell,” then cleaned up the data by selecting all hits from a certain patent classification. They also collected the extended patent family to consider all possible innovations and their citations. The final dataset had 9,328 patents.

From this data, the authors constructed a network of patents (nodes) and citations (edges). They selected the largest connected subnetwork (5,029 nodes) and then traced a patent development path based on the citation directionality, which resulted in 735 highly cited patents. Next, the authors performed k-core analysis to identify three subnetworks in the remaining nodes that corresponded with three different technological developments: thin-film, organic, and crystalline silicon (see below). Also, they analyzed the networks based on subset of years and found trends in the convergence of technologies.

The authors used the results to predict change in the industry for on the most rapidly growing technology based on the most highly cited patents. They also identified “hidden” technologies within each subnetwork that might become the disruptive technology.

While the paper presented a positive outcome, the authors did warn of several limitations. For example, their analysis depended on inventors seeking patents to be included in the sample. They also suggested that their method needs to be applied to other industries for testing.



Habermas and Castells

Jurgen Habermas defined the public sphere as a separate space where informed people debate social and political issues, form public opinion, and influence the state and society. In a democratic society, the public sphere ideally allows for everyone to have access to information and be able to participate equally in discussions. His vision allows for an open public sphere, although the reality might constrain participation for certain segments of society who may not have enough ability or resources. In the past, this became evident in the dominance of the bourgeoisie who came to salons and coffeehouses to discuss societal issues, which largely excluded the working class and sometimes women.

Manuel Castells declared that society has moved from the Industrial Revolution (production of material goods) to the Information Age (knowledge economy). The network society has been enabled by current technologies (such as smartphones and internet). Communication is based on an open structure network, which breaks down some of the traditional social hierarchies and national borders because information flows almost anywhere (China’s state censorship might be a notable exception). Different participants might have different value within the network, such as highly connected individuals.

Castell’s theory operates within the idea of the public sphere by somewhat eliminating time and space. Electronic communication is instantaneous and possible with anyone across the world. It also could used to communicate with individuals or communities, which could known or unknown. However, his perception of “timeless time” may seem like digital networks allow for disruption of the flow of linear time, but I would argue that multi-tasking is not new or unique to the digital age. Time even may gain linear importance in terms of “keeping up” with the latest news and trends. For Twitter, a single tweet might get lost among 6,000 tweets a second if a user doesn’t have many followers (network connectedness) or a particular hashtag isn’t trending.  In addition, network theory is compatible with traditional local and in-person networks.

The major effect from the network society has been increased participation and access to information. Want a graduate degree? Take online classes. The federal government has piloted public participation in coding through Github. Politicians get fewer letters and phone calls from constituents, but more emails and contacts from social media. The flow of digital information has increased from a river to a flood, which may be the greatest downside to the digital revolution.  Now someone might be able to search online for health symptoms and get hundreds of possibilities from various websites. Dr. Google will present mild possibilities from the common cold to deadly illnesses along with suggestions for folk remedies. Which source do you trust: the Mayo Clinic (based on their brick-and-mortar reputation) or the Wellness Mama blogger?

Castell is onto something that others have suggested: the form of the media matters. Habermas appears mostly concerned about the ability of the mass media to inform the public sphere and act as a good intermediary. Marshall McLuhan (infographic below) also argues that the medium fundamentally affects our ability to communicate. For example, the “tribal era” is characterized by an oral tradition of memorization and listening to storytelling, which is limited to a local community. The print era allows for the dissemination of more materials, but actually limits communication to a one-way exchange of ideas (from print to reader). Television expands the capability of the print era in being able to reach an even larger audience with only slightly more interaction than print (such as telephone interviews or arranging live appearances). The digital age finally expands the ability for participation: either one to one, one to many, or many to many. This is the root cause of why the digital age seems so remarkable to Castell.

in trends

Omi and Winant (2014) “stress that race is a social construction, and not a fixed, static category rooted in some notion of innate biological differences.” Social, economic, and political forces, across time and place, have influenced racial meanings. Many scientists hoped that mapping our genome would provide definitive evidence that there is no such thing biologically as race. Humans share over 99% of the same genome.

However, some scientists have chosen to concentrate on the last partial percentage as potentially significant genetic variation due to racial or ethnic differences. In an ideal world, scientists envisioned that these studies would target diseases and treatments for particular populations. However, this has led some to argue that a biological basis for race really exists, which also might result in rather ugly social consequences, such as rationalizing racism.

An example of a widely available image online (below) shows a statistical summary of genetic data that clusters according to the countries in Europe. While the original scientific article does not mention race, the authors found a close correspondence between genes and geography. They believed that these results supported the accuracy of genetic ancestry testing based on a sample of 1,376 Europeans. This image reappears on science blogs and as evidence on popular forums about similarities between ethnic groups based on the distance shown on the map.

Shiao, Bode, Beyer, and Selvig (2012) presented a theoretical synthesis between recent genetic ancestry research and the social construction of race. The authors accepted that statistically identifiable clusters of genes seem homologous to certain racial and ethnic classifications. However, any identifiable biological basis for race and ethnicity cannot be separated from its complex social construction across time and place.

Duster (2015) urged social researchers to critically examine the social assumptions about race that have been transmogrified as science and trace their actual origins in social, historical, and folk categories of race. Some scientists have stated that genetics research studying differences between populations is scientifically appropriate, but assigning value such as genetic superiority to their findings is politics, not science. Instead, Duster argued that politics cannot be separated from science because the use of racial and ethnic categories originally come from political taxonomies. He also cautioned against ancestry testing and admixture research (a combination of genetic lineages) as fundamentally flawed science as well as depending on political history to define populations. Ancestry testing relies on “reference populations” of contemporary people along with assumptions about migrations, reproductive patterns, and historical events. The actual computer models determining an individual’s results tend to be a “black box” proprietary to the companies marketing a particular test. For example, a customer might receive results that provide the appearance of precision (example below). This “professional genetic genealogist and television consultant” provides a review of four consumer tests for determining admixture, which shows differing results across the companies.

In 2014, USA Today published an opinion piece about the genealogy “craze” as the second most popular hobby and second most visited category of website (after pornography). While the author refers to the digitization of databases as the popularizing factor, consumer genetic testing also appeals to these hobbyists.

Duster believed that these results should be qualified with more caveats. For example, for a person expecting certain results, the company’s sample might not include that population. There also is no guarantee that a match of genetic markers to a certain population or geographical area means that the individual has a close affinity to a racial or ethnic group.

Duster’s article sparked controversy in the social sciences and he published a response to seven of his commentators (2015). In particular, the intersection between science and society is visible in the question of when, rather than where, an individual might claim ancestry. For example, Italy became a unified nation state in 1858. Prior to these dates, ethnic categories would be based on regional admixtures including Milanese, Roman, and Neapolitan. How do scientists determine the appropriate admixture for a single ethnic category of Italian?

He also presented the Boers in South Africa in the mid-sixteenth century as another example requiring social context. A white person born in Africa with grandparents of this ancestry would be African. However, the observable appearance of whiteness would outweigh the “scientifically neutral” ancestry analysis. Other researchers have proven that the phenotype determines results more than genotype.

The Guardian recently published an article about the booming consumer genetic testing market, worth $70 million in 2015 and expected to rise to $340 million by 2022 (chart below by Credence Research cited in the article). In addition to ancestry, consumers hope to understand the relationship between their genes and health, which might include their response to a particular type of exercise and diet.

In addition, Duster warned against the potential problems in social scientists and geneticists collaborating on research. Social scientists may not understand the scientific details of the genetics research in order to question the assumptions, which lends multi-disciplinary legitimacy to the collaboration and normalizes the science. Genomic advocates also claim benefit in new medication targeting certain populations, but this helps rationalize racial hierarchies. Who should benefit from targeted research? Marmot (2005) attributed social factors as the root cause of health inequalities, such as significant differences in life expectancy and treatment of disease. Gravlee (2009) stated that “social inequalities shape the biology of racialized groups, and embodied inequalities perpetuate a racialized view of human biology.” House (2015) recently reiterated that social policies to improve socioeconomic determinants of health would be necessary to reverse worsening health outcomes and reduce health spending in the United States. For example, the following chart (below) shows poverty rates among senior citizens by race and ethnicity in a report looking at Medicare beneficiaries conducted by the Kaiser Family Foundation.

Yudell, Roberts, DeSalle, and Tishkoff (2016) called for a systematic effort to address issues concerning the use of race in genetic research. The authors distinguished between ancestry as “a process-based concept, a statement about an individual’s relationship to other individuals… a very personal understanding of one’s genomic heritage” versus race as “a pattern-based concept… which connect an individual to a larger preconceived geographically circumscribed or socially constructed group.”

Where does this leave a consumer curious about their ancestral heritage and health-related consequences of their genome? Scientists would be the first to admit that their research is based on limited opportunity samples (present-day volunteers) and statistical correlations, rather than absolute certainty. However, companies marketing their consumer genetic tests seem unlikely to mention the assumptions and potential inaccuracies in their product. These companies also profit from selling the data collected from their customers to pharmaceutical and biotech firms. In addition, sociologists should continue to discuss the social and historical basis for determining race and racism, which rejects a biological explanation as insufficient for the complexity in these issues. There also is room for further intersectional analysis of the socioeconomic determinants of health in determining future directions for genetic research to improve health in disadvantaged populations.


Duster, T. (2015a). A post-genomic surprise. The molecular reinscription of race in science, law and medicine. The British Journal of Sociology, 66(1), 1–27.
Duster, T. (2015b). Response to comments on ‘A post-genomic surprise.’ The British Journal of Sociology, 66(1), 83–92.
Gravlee, C. C. (2009). How race becomes biology: Embodiment of social inequality. American Journal of Physical Anthropology, 139(1), 47–57.
House, J. S. (2016). Social Determinants and Disparities in Health: Their Crucifixion, Resurrection, and Ultimate Triumph(?) in Health Policy. Journal of Health Politics, Policy and Law, 41(4), 599–626.
Marmot, M. (2005). Social determinants of health inequalities. The Lancet, 365(9464), 1099–1104.
Novembre, J., Johnson, T., Bryc, K., Kutalik, Z., Boyko, A. R., Auton, A., … Bustamante, C. D. (2008). Genes mirror geography within Europe. Nature, 456(7218), 98–101.
Omi, M., & Winant, H. (2014) Racial Formation in the United States, 3rd edition. Routledge.
Shiao, J. L., Bode, T., Beyer, A., & Selvig, D. (2012). The Genomic Challenge to the Social Construction of Race. Sociological Theory, 30(2), 67–88.
Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016). Taking race out of human genetics. Science, 351(6273), 564–565.

in pictures

The Human Genome Project held great promise for advances in medicine, but also had greater societal implications. Many scientists hoped that mapping our genome would provide definitive evidence that there is no such thing biologically as race. Other researchers argued that race is a biological concept and real differences exist between races. For example, Race, Evolution, and Behavior: A Life History Perspective by J. Phillipe Rushton (1995) claimed the existence of at least three basic races with the following controversial average differences:

However, other research argues that socioeconomic inequalities have been the root cause of perceived and actual differences between races. For example, the following table indicates one effect of low socioeconomic status and race on life chances, specifically going to college. The chart, however, is not meant to dismiss the cost of disadvantage to being black in comparison to low socioeconomic status because it is citing the gap in student scores between blacks and whites (not an apples to apples comparison). The context of the web article using the chart is for achieving better diversity in higher education through affirmative action. As a result, the takeaway is that the combination of race and low socioeconomic status results in significant disadvantages that would benefit from class-based affirmative action programs.

Genetics research held the potential to “settle” these arguments, but it ultimately resulted in greater uncertainty. Duster (2015) describes the “inadvertent and unintended spin-offs” of genetics research through racial and ethnic markers in forensics to solve crimes, marketing unreliable ancestry analysis based on low validity samples, targeting clinical treatments for specific populations, or looking for genetic causes for certain diseases in specific populations. (p.4) He brings up the concern that racial assumptions heavily rely on “social, historical, and folk categories, but are then transmogrified into the language of science and anointed with an imprimatur of legitimacy.” (p.23)

This leads to discovering the following graphically unsophisticated infographic (with slight variation as individual slides) of unknown origin floating around on online discussion boards and message forums, usually as “proof” about the biological basis for race. The infographic presents a danger on social media because the slides make a claim for credibility with scientific charts and academic citations. (The myths have been reordered below into groupings for further discussion).

  • Myth #1: Race has no biological basis.
  • Myth #3: Race is only skin-deep.
  • Myth #4: Races have more variation within them than between them.
  • Myth #6: There isn’t significant genetic difference between races.
  • Myth #7: There isn’t qualitative genetic evidence for racial differences.

Frank (2015) discusses that over 99.9% of the human genome is the same. However, ancestry testing companies have marketed their findings as definitive while their scientific basis is problematic. Biogeographical ancestries compare an individual’s genes to a sample of present-day populations of geographical regions. As Duster also mentions, statistical assumptions have been made that would appear no more “logical” or “objective” than socially constructed definitions of race.

  • Myth #2: Race is a social construct.

Frank admits that there seems to be a weak correlation between biogeographical ancestry and race. However, she also points out that genetic researchers heavily rely upon social definitions of race for “creating, informing, and interpreting supposedly value-neutral genetic facts about the nature of human variation.” (p.58)

According to Shiao, et. al. (2012), a theoretical synthesis is possible between the social construction of race and the categorization of statistically discernible alleles into clinal classes.  These classes would be the outliers in “otherwise continuous genetic variation, similar to social classes in otherwise continuous economic variation.” (p.69) Rather than completely rejecting the idea of genetics in racial constructionism, the authors also suggest  “a version of the feminist distinction between biological sex and socially constructed gender. (p.72)

  • Myth #5: Racial differences in intelligence are explained by socio-economic factors.
  • Myth #8: Individual successes disprove the relevance of race.

As mentioned before, socioeconomic and racial factors affect an individual’s life chances resulting in stratification. Advantages and disadvantages often continue onto the next generation of children. Most of the charts shown capture information from one point in time. A gap exists between white and black achievement “at all levels.” However, what would a generational study show? Would educational achievement be increased in children if their parents had done better than the previous generation, etc.?

Why does this matter? Phelan, et. al. (2013) used surveys to test two competing vignettes in news stories: race as genetic versus race as social construction. The authors found that because news articles about race, health, and genetics have become commonplace enough seem neutral, which “circumvents our usual tendency to check incoming persuasive messages against our preexisting social attitudes; and that the public generalizes messages about specific, genetically based racial differences in health to broader, more fundamental or essential, genetic differences between racial groups.” (p.185) The sociological challenge is that “race” brought up most of the time has its basis in social construction. Genetically based race has more limited and specific uses, such as medical research. The sociological challenge may be to reinforce this distinction that most messages about race will be a commentary about society and not genetics, while continuing to navigate the cultural sensitivities around the topic.


Duster, Troy. 2015. “A post-genomic surprise. The molecular reinscription of race in science, law and medicine.” The British Journal of Sociology 66:1–27.

Frank, Reanne. 2015. “Back to the Future? The Emergence of a Geneticized Conceptualization of Race in Sociology.” The ANNALS of the American Academy of Political and Social Science 661:51–64.

Phelan, Jo C., Bruce G. Link, and Naumi M. Feldman. 2013. “The Genomic Revolution and Beliefs about Essential Racial Differences: A Backdoor to Eugenics?” American Sociological Review 78:167–191.

Shiao, J., Bode, T., Beyer, A., & Selvig, D. (2012). The Genomic Challenge to the Social Construction of Race. Sociological Theory, 30(2), 67-88.

SNA project proposal

For my first social network analysis project, I selected Neil deGrasse Tyson as a famous scientist on Twitter to create an egocentric network. He follows far fewer entities (43) than those that follow him (9.41 million). I’m curious about the significance of the entities that he has chosen to follow and any interrelationships between them. Other famous scientific figures (and celebreties) seem to follow similar patterns of following few entities while having many followers, so this would suggest that these users obtain limited information from their news feed and primarily use tweets to disseminate information. Other randomly selected famous scientists include Carolyn Porco, Cassini imaging lead, following 342 and 57.6k followers; Michio Kaku, physicist, following 51 and 622k followers; or Richard Dawkins, biologist, following 368 and 2.46 million followers. The first two scientists might intersect with Tyson’s world, but Dawkins is a controversial figure in a different field (biology and evolution). However, Dawkins is included in the 43 as well as Pee Wee Herman (entertainer), while Porco and Kaku have not be followed. However, Tyson did mention Porco in a tweet about Cassini’s end (Sept 19), which had been retweeted 548 times.

The most basic research question is what does an egocentric network of a famous scientist look like? What types of entities does he choose to follow and can they be classified into a few buckets: other scientists, media organizations, government agencies, any unknown individuals, or random famous individuals (Pee Wee Herman)? Would this information support a hypothesis for a larger project that Twitter might be similar to LinkedIn for certain types of users: a professional network rather than friends and family? And the sociological research question: is there a correlation between entities followed and status (such as famous people with a threshold of followers in the millions)? For example, does it indicate greater status for Tyson to mention Porco in a tweet, but she doesn’t have enough status to be followed?

The relationships between the ego and alters may be supplemented through other manual research to discover more information (such as publishing a scientific paper together using a scholarly database, appearing on the same panel at a conference, or a media appearance for a media entity). In addition, other data points may be gathered from a sampling of retweets and mentions for the last week.

I’m hoping to see some clusters based on the buckets of entities. For the example, the scientists might be linked to each other through social media ties or academic work. The comedians might be linked together by appearing in the same event with a science theme or  linked to the other scientists through a media appearance (such as Stephen Colbert’s show). Charles Kadushin, Graham Wright, Michelle Shain, and Leonard Saxe had an interesting study of social integration for young American Jews. It reminded me that these clusters tend to be based on homophily, creating sub-networks. Also, I’m wondering if Tyson’s network might result in a small world model (like the previously discussed six degrees of separation and Kevin Bacon).

Kreb’s article on “Mapping Networks of Terrorist Cells” makes the point that the network may be incomplete because I can’t simply ask why for following certain entities and also dynamic because the network has increased by 1 since last week.  In addition, Kadushin (2012) brings up an ethical question about social network research: would Tyson be annoyed at being the focus of this network project or be amused?

Node centrality measures

Robins (2015) lists the different measures of node centrality:

  • Degree centrality shows the node with the most connections (edges). Who is the most connected individual in the crime network?
  • Betweenness is the importance of a node in connecting the network. In other words, betweenness identifies nodes that make bridges to otherwise disconnected nodes. Removing these nodes might break the network or reduce its size.
  • Closeness is the sum of distances from one node to all other nodes, which would show how quickly information might flow through the network.
  • Eigenvector indicates whether a node is connected to well-connected nodes. In other words, a node might not be well connected, but it’s connected to other well connected nodes (so don’t make them mad anyway).
  • Beta shows the total number of paths to get from one node to the others, which indicates power or influence.

The above diagram illustrates the node centrality measures, except for beta. Researchers may choose a few particularly relevant node centrality measures, such as degree and betweenness used together. Others may not be as relevant for their network and the desired measured effect, so it is dependent on situation. For example:

“Examining the network components of a Medicare fraud scheme: the Mirzoyan-Terdjanian organization” by Travis Meyers examined a white-collar transnational crime organization that stole more than $100 million from Medicare. Using social networking analysis through archival data, he examined the structure of the organization using degree centrality and betweenness centrality. Meyers deemed closeness centrality not as “pertinent within criminal networks given their unclear and often fuzzy boundaries,” but still helpful for interpreting ties between nodes. He also used brokerage measures “to determine the actors who are in advantageous positions to broker the flow of resources to various sects of the network.” The study hoped to show the effectiveness in social networking analysis uncover criminal activity and reduce fraud.

John Tawa, Ruqian Ma, and Shinji Katsumoto conducted an experiment using avatars in Second Live in ‘‘All Lives Matter’’: The Cost of Colorblind Racial Attitudes in Diverse Social Networks.The authors attributed colorblind racial attitudes, rather than outgroup prejudice, as directly related to lower levels of closeness centrality and clustering within the network. In other words, colorblind racial attitudes had adverse effects on relationships in their participants. The authors used closeness centrality, betweenness centrality, degree centrality, and clustering coefficient (how much a node has connections with mutual connections). The methodology also used a  scale for colorblind racial attitudes, physical measures of virtual distance within the game, and participation in chatting.

Genetics and the social construction of race bibliography

My topic has meandered through science and settled on the discussion of what genetic research means for race. Here’s the working bibliography organized by searches. I find the political and legal ramifications an interesting effect, the “so what” factor of why the possibility the genetic research might define racial differences or reaffirm the social construction of race.

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