Joan Acker and the Shift from Patriarchy to Gender

by: Tristan Bridges and James W. Messerschmidt

We’ve read some of the tributes to the feminist sociological genius of Joan Acker.  And much of that work has celebrated one specific application of her work.  For instance, Tristan posted last week on Acker’s most cited article—“Hierarchies, Jobs, Bodies: A Theory of Gendered Organizations” (1990)—which examined the ways that gender is so embedded in the structure of organizations that we often fail to appreciate just how much it shapes our lives, experiences, and opportunities.  But, this specific piece of her scholarship was actually her applied work. It was an application of a theoretical turn she was suggesting all sociologists of gender follow.  And we did.  Acker was involved in an incredibly important theoretical debate that helped shape the feminist sociology we practice today.

“Patriarchy” is a concept that is less used today in feminist social science than it was in the late-1970s and 1980s.  The term has a slippery and imprecise feel, but this wasn’t always the case. There were incredibly nuanced debates about patriarchy as a social structure or as one part of “dual systems” (capitalism + patriarchy) and exactly what this meant and involved theoretically. Today, we examine “gender.”  Indeed, the chief sociological publication is entitled Gender & Society, not Patriarchy & SocietyAcker - The Problem with PatriarchyBut in the 1970s and 1980s, patriarchy was employed theoretically much more often.  Feminist scholars identified patriarchy to focus the critique of existing theoretical work that offered problematic explanations of the subordination of women.  As Acker put it in “The Problem with Patriarchy,” a short article published in Sociology in 1989: “Existing theory attributed women’s domination by men either to nature or social necessity rather than to social structural processes, unequal power, or exploitation” (1989a: 235). The concept of patriarchy offered a focus for this critique.

Joan Acker was among a group of scholars concerned about the limitations of this focus; in particular, patriarchy was criticized for being a universal, trans-historical, and trans-cultural phenomenon—“women were everywhere oppressed by men in more or less the same ways” (1989a: 235).  Concluding that patriarchy could not be turned into a generally useful analytical concept, Acker proposed that feminist social science move in a different direction—a route that was eventually largely accepted and taken up.  It’s no exaggeration to suggest that Acker was among a small group of feminist scholars who shifted the conversation in an entire field.  We’ve been relying on their suggestion ever since.

Acker’s short 6-page article was published in the same journal that had published Raewyn Connell’s article, “Theorizing Gender” (1985), which spelled out her initial delineation of the problems with sex role theory and what she labeled “categoricalism.” Connell was also concerned with how feminist theories of patriarchy failed to differentiate among the categories of “women” and “men”—that is, femininities and masculinities. Judith Stacey and Barrie Thorne’s “The Missing Feminist Revolution in Sociology” (in Social Problems) was published that year as well (1985), specifically criticizing sociology for solely including gender as a variable but not as a theoretical construct. Acker (1989a) explained why feminist social scientists ought to follow this trend and shift their focus from patriarchy to gender relations and the construction of gender in social life.  As Acker wrote, “From asking about how the subordination of women is produced, maintained, and changed we move to questions about how gender is involved in processes and structures that previously have been conceived as having nothing to do with gender” (1989a: 238).  And in another piece published in the same year—“Making Gender Visible” (1989b) in the anthology, Feminism and Sociological Theory—Acker argued for a paradigm shift that would place gender more centrally in understanding social relations as a whole. Acker suggested a feminist theoretical framework that was able to conceptualize how all social relations are gendered—how “gender shapes and is implicated in all kinds of social phenomena” (1989b: 77). Today, this might read as a subtle shift.  But it was monumental when Acker proposed it and it helped open the door too much of what we recognize as feminist sociology today.

Acker published what became her most well-known article—“Hierarchies, Jobs, Bodies”—in Gender & Society (1990) as an illustration of what the type of work she was proposing would look like.  She was concerned with attempts that simply tacked patriarchy onto existing theories which had been casually treated as though they were gender-neutral.  She explained in detail how this assumption is problematic and limits our ability to understand “how deeply patriarchal modes are embedded in our theorizing” (1989: 239).  And Acker illustrated this potential in her theorizing about gender in organizations.  But her suggestion went far beyond organizational life.

And by all measures, we took up Acker’s suggestion:  “Gender,” “gender relations,” and “gender inequality” are now the central foci of sociological theory and research on gender.  But Acker also concluded her short 1989 article with a warning.  She wrote,

[T]here is a danger in abandoning the project of patriarchy.  In the move to gender, the connections between urgent political issues and theoretical analysis, which made the development of feminist thought possible, may be weakened.  Gender lacks the critical-political sharpness of patriarchy and may be more easily assimilated and coopted than patriarchy. (1989a: 239-240)

Certainly, Acker’s concern leads us to honestly ask: Will shifting the theoretical conversation from patriarchy to gender eventually result in simply a cursory consideration of gendered structured inequality? Will the shift to gender actually loosen our connections with conceptualizations of gendered power? We don’t think so but one way to commemorate the legacy of Joan Acker is to both celebrate gender diversity while simultaneously visualizing and practicing gender equality.  This means continuing to recognize that inequality is perpetuated by the very organization of society, the structure of social institutions, and the historical contexts which give rise to each.

___________________________
References
Acker, Joan. 1989a. “The Problem with Patriarchy.” Sociology 23(2): 235-240.
Acker, Joan. 1989b. “Making Gender Visible.” Pp. 65-81 in Wallace, P.A., Ed., Sociological Theory and Feminism. Newbury Park, CA: Sage.
Acker, Joan. 1990. “Hierarchies, Jobs, Bodies: A Theory of Gendered Organizations.” Gender & Society 4(2): 139-158.
Connell, Raewyn. 1985. “Theorising Gender.” Sociology 19(2): 260-272.
Stacey, Judith and Barrie Thorne. 1985. “The Missing Feminist Revolution in Sociology.” Social Problems 32(4): 301-316.

Google, Tell Me. Is My Son Gay?

Originally posted at Feminist Reflections.

Screen-Shot-2016-06-01-at-3.40.39-PM-300x290In 2014, a story in The New York Times by Seth Stephens-Davidowitz went viral using Google Trend data to address gender bias in parental assessments of their children—“Google, Tell Me. Is My Son a Genius?”  People ask Google whether sons are “gifted” at a rate 2.5x higher than they do for daughters.  When asking about sons on Google, people are also more likely to inquire about genius, intelligence, stupidity, happiness, and leadership than they are about daughters.  When asking about daughters on Google, people are much more likely to inquire about beauty, ugliness, body weight, and just marginally more likely to ask about depression.  It’s a pretty powerful way of showing that we judge girls based on appearance and boys based on abilities.  It doesn’t mean that parents are necessarily consciously attempting to reproduce gender inequality.  But it might mean that they are simply much more likely to take note of and celebrate different elements of who their children are depending on whether those children are girls or boys.

To get the figures, Stephens-Davidowitz relied on data from Google Trends. The tool does not give you a sense of the total number of searches utilizing specific search terms; it presents the relative popularity of search terms compared with one another on a scale from 0 to 100, and over time (since 2004).  For instance, it allows people selling used car parts to see whether people searching for used car parts are more likely to search for “used car parts,” “used auto parts,” or something else entirely before they decide how to list their merchandise online.  I recently looked over the data the author relied on for the piece.  Stephens-Davidowitz charted searches for “is my son gifted” against searches for “is my daughter gifted” and then replaced that last word in the search with: smart, beautiful, overweight, etc.

And while people are more likely to turn to Google to ask about their son’s intelligence than whether or not their daughters are overweight, people are much more likely to ask Google about children’s sexualities than any other quality mentioned in the article.  And to be even more precise, parents on Google are primarily concerned with boy’s sexuality.  Below, I’ve charted the relative popularity of searches for “is my son gay” alongside searches for “is my daughter gay,” “is my child gay,” and “is my son gifted.”  I included “child” to illustrate that Google searches here are more commonly gender-specific.  And I include “gifted” to illustrate how much more common searches for son’s sexuality is compared with searches for son’s giftedness (which was among the more common searches in Stephens-Davidowitz’s article).Picture1The general trend of the graph is toward increasing popularity.  People are more likely to ask Google about their children’s sexuality since 2004 (and slightly less likely to ask Google about their children’s “giftedness” over that same time period).  But they are much more likely to inquire about son’s sexuality.  At two points, the graph hits the ceiling.  The first, in November of 2010, corresponds with the release of the movie “Oy Vey! My Son is Gay” about a Jewish family coming to terms with a son coming out as gay and dating a non-Jewish young man.  The second high point, in September of 2011, occurred during a great deal of press surrounding Apple’s recently released “Is my son gay?” app, which was later taken off the market after a great deal of protest.  And certainly, some residual popularity in searches may be associated with increased relative search volume since.  But, the increase in relative searches for “is my son gay” happens earlier than either of these events.

Relative Search Popularity

Indeed, over the period of time illustrated here, people were 28x more likely to search for “is my son gay” than they were for “is my son gifted.”  And searches for “is my son gay” were 4.7x more common than searches for “is my daughter gay.”

Reading Google Trends is a bit like reading tea leaves in that it’s certainly open to interpretation.  For instance, this could mean that parents are increasingly open to sexual diversity and are increasingly attempting to help their children navigate coming to terms with their sexual identities (whatever those identities happen to be).  Though, were this the case, it’s interesting that parents are apparently more interested in helping their sons navigate any presumed challenges than their daughters.  It could mean that as performances of masculinity shift and take on new forms, sons are simply much more likely to engage with gender in ways that cause their parents to question their (hetero)sexuality than they used to.  Or it could mean that parents are more scared that their sons might be gay.  It is likely all of these things.

I’m not necessarily sold on the idea that the trend can only be seen as a sign of the endurance of gender and sexual inequality.  But one measure of that might be to check back in with Google Trends to see if people start asking Google whether their sons and daughters are straight.  At present, both searches are uncommon enough that Google Trends won’t even display their relative popularity.

The Architecture of Gentrification; Or, The Dining Rooms are Coming

By: Lisa Wade
Originally posted at Sociological Images.

The dining rooms are coming. It’s how I know my neighborhood is becoming aspirationally middle class.

My neighborhood is filled with “shotgun” houses. Probably from West Africa, they are designed for a hot, humid climate. The homes consist of several rooms in a row. There are no hallways (and no privacy). High ceilings collect the heat and the doorways are placed in a row to encourage a breeze to blow all the way through.

Around here, more often than not, they have been built as duplexes: two long skinny houses that share a middle wall. The kitchen is usually in the back leading to an addition that houses a small bathroom. Here’s my sketch:

20160529_115024

As the neighborhood has been gentrifying, flippers have set their sights on these double shotguns. Instead of simply refurbishing them, though, they’ve been merging them. Duplexes are becoming larger single family homes with hallways (which substantially changes the dynamic among its residents) and makes space for dining rooms. Check out the new dining room on this flip (yikes):

8-6

At NPR, Mackensie Griffin offered a quick history of dining rooms, arguing that they were unusual in the US before the late 1700s. Families didn’t generally have enough room to set one aside strictly for dining. “Rooms and tables had multiple uses,” Griffin wrote, “and families would eat in shifts, if necessary.”

Thomas Jefferson would be one of the first Americans to have a dining room table. Monticello was built in 1772, dining room included. Wealthy families followed suit and eventually the trend trickled down to the middle classes. Correspondingly, the idea that the whole family should eat dinner together became a middle class value, a hallmark of good parenting, and one that was structurally — that is, architecturally — elusive to the poor and working class.

The shotgun house we find throughout the South is an example of just how elusive. Built before closets, all the rooms in a traditional shotgun are technically multi-purpose: they can be used as living rooms, bedrooms, offices, dining rooms, storage, or whatever. In practice, though, medium to large and sometimes extended families live in these homes. Many residents would be lucky to have a dedicated living room; a dining room would be a luxury indeed.

But they’re coming anyway. The rejection of the traditional floor plan in these remodels — for being too small, insufficiently private, and un-dining-roomed — hints at a turn toward a richer sort of resident, one that demands a lifestyle modeled by Jefferson and made sacred by the American middle class.

__________________________________

Lisa Wade, PhD is a professor at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. Follow Dr. Wade on TwitterFacebook, and Instagram.  She also blogs at Sociological Images, where this post originally appeared.

Visualizing the Sociology of Liana Sayer and Time Use Research

Originally posted at Feminist Reflections.

People are often shockingly wrong about how much time they dedicate to various tasks.  In general, we tend to overestimate how much time it takes to do things we dislike and underestimate how much time we spend on tasks we enjoy.  So, people ritualistically overestimate how much time they spend on laundry, cleaning bathrooms, working out and underestimate how much time they spend watching television, napping, eating, or doing any number of tasks that provide them with joy.

Asking about how people use their time has been a mainstay on surveys dealing with households and family life.  We ask people to assess how much time they spend on all manner of mundane tasks in their lives–everything from shopping, sleeping, watching television, attending to their children, and household labor is divided up into an astonishing number of variables.  The assumption, of course, is that people can provide meaningful information or that their responses are an accurate (or approximately accurate) portrayal of the time they actually spend (see here).  This is why time diary studies came into being–they produce a more accurate picture of how people use their time.  People record their actual time use throughout the day in a diary, marking starting and stopping points of various activities.  And there are a number of different scholars who rely on this method and these data.  But Liana Sayer is among the leading scholars in the field.  When I’ve seen her present, or others present on time use data, the data are almost always visualized in the same way (as stacked column charts).  Personally, I love seeing the data this way.  The changes jump out at me and I feel like I instantly recognize trends and distinctions they discuss. But I have learned in my classroom that students do not always have the same reaction.

I’m interested because I use data visualizations in class a lot.  And in my (admittedly limited) experience, students have an easier time interpreting the story of time use data when it is visualized in some ways over others.

All of the examples here are pretty basic changes in data visualizations.  But, learning these basics are necessary to help students read the more complex data visualizations they may encounter.  Being able to interpret visualizations of temporal data is important; it’s part of what helps social scientists consider, measure, and critique the idea of “feminist change.”  Distinguishing between men’s and women’s time use is only one pocket of this field.  But, it’s the one I’ll focus on here, and on which Liana Sayer is among the foremost experts.  The data I’m visualizing below come from one of Sayer’s most cited articles: “Gender, Time and Inequality: Trends in Women’s and Men’s Paid Work, Unpaid Work, and Free Time” (here – behind a paywall).

It’s fairly common to present time use data with a series of stacked columns (the same way the Census often illustrates shifts in household types).  Below is a visualization of the differences between women’s and men’s minutes per day allocated to paid work, unpaid work, free time, and time dedicated to self care.  It’s all time diary data and we talk about why this is more reliable and a better measure – but also why it is more difficult to collect, etc.  Some students see the story of this graph immediately.  I do too.  Men’s time allocated to paid labor decreased while women’s increased.  And women perform more unpaid work than do men.  Lots of students, however, are stumped. stacked column

But when I present the data differently, students often have an easier time seeing the story the chart is produced to illustrate.  The Pew Research Center visualizes a lot of their data using stacked bar graphs.  And maybe it’s because these are more easily recognized by people with less experience with data visualization.  I have found that more of my students are able to read the chart below than the one above (at least for temporal time use data comparisons).stacked bar

Another way of presenting these data might be to use clustered columns.  I have also found that students are more quick to recognize the trend in these data with the graph below than they are with the initial stacked column chart.cluster column

But, I’ve found that students have the easiest time with line charts for temporal time use data.  On the chart below, I deleted the grid lines because The New York Times sometimes displays time trend data this way (see Philip Cohen’s piece on NYT Opinionater, “How Can We Jump-Start the Struggle for Gender Equality?” for an example).  Students that struggle to recognize the trend in the clustered column chart, are much faster to see the trend here.line

These aren’t an exhaustive set of examples, and all of them are basic visualizations.  For instance, we might use a stacked area chart to show these data (as trends in the racial composition of the U.S. are often depicted), a scatterplot (as data on GDP and fertility rates are often illustrated), a series of pie charts (as men’s and women’s various compositions in different economic sectors are sometimes visualized), or something else entirely. Screen Shot 2016-03-22 at 9.59.28 AMIn fact, The New York Times produced a really incredible interactive stream graph visualizing data from The American Time Use Survey that illustrates differences in time use between groups. I sometimes have students explore this graph in my course on the sociology of gender.  But many struggle interpreting it.  This is, I think, in part due to the fact that we often take data visualization literacy for granted.  It’s a skill, and it’s one we should be better at teaching.

I think the point I want to make is that we (or I, at least) need to think more carefully about how we visualize our data and findings to different audiences.  Liana Sayer has an incredible mastery of this (she presents data in all of the ways mentioned in this post and more throughout her work).  One thing that I’m thinking more about as I write and teach about research and findings amenable to data visualization is which visualizations are best suited to which kinds of data (something all scientists are concerned with), but also which visualizations work with which kinds of audiences.  This is new territory to me.

Visualizing feminist change in a single chart is difficult.  And it’s often accompanied by, “Well, this is true, but let me tell you about what these data don’t show…”  But, I’m interested in how we make choices about visualizing feminist research and whether we need to make different kinds of choices when we talk about the findings with different kinds of audiences.

Why Popular Boy Names are More Popular than Popular Girl Names

Originally posted at Feminist Reflections.

In my introduction to Sociology class, I use trends in baby names to introduce students to sociological research and inquiry. It’s a fun way to show students just how much we can learn from what might feel like idiosyncratic details of our lives. I start by showing students the top 10 boy and girl names from the most recent year of data available (along with their relative frequencies). After this, I show them the most popular names and their relative frequencies from 100 years earlier. There are some names on both lists; but for the most part, the names on the latter list sound “old” to students. Screen Shot 2016-02-19 at 12.37.09 PM

When I ask students to characterize the types of names they see on the older list of names, someone usually says the names sound more “traditional.” I tell them that in 100 years, someone will probably say that about the most recent list of names they’re looking at: these future students will have a different idea of what makes a name “traditional.”

eva4ptth553dlbgtfzjf

19fed3usy9vmzgif

If you’re interested, someone produced these two GIF files that depict the most popular names by state between 1960 and 2012. I like to show one of these while I’m talking with students about what what names can help us learn. I ask students to raise their hand when they see their own name or the name of their best friend. As we get into the 80’s and 90’s, lots of hands start going up. But the GIFs are also interesting because they are a powerful visualization of the spread of cultural norms. Popular names move through a population in a way that appears to be similar to infectious diseases.

This is a fun way to show students that deciding what to name a child might feel like a personal decision, it’s actually a decision that is shaped by social forces. Names and name trends are great examples of what sociology can reveal because, as Stanley Lieberson points out so simply, while taste in most elements of culture is not a requirement, everyone has tastes in names. And, as it turns out, we can learn a lot about a society just by looking at patterns in which names we select for our children (and equally important are the types of names different groups tend to avoid).

SIDENOTE: I like to highlight a great finding by Stanley Lieberson, Susan Dumais, and Shyon Baumann from their article on trends in androgynous names (here). Androgynous names are names that are given to both boys and girls–think Taylor, Cameron, or Casey for current examples. Lieberson, Dumais, and Baumann found that androgynous names follow an incredibly common pattern once they achieve a critical level of popularity: they become girl names and become dramatically less common names for boys–a powerful example of the stigma associated with femininity for boys.

When I first started using the exercise, I was fascinated with the relative frequencies much more than the names on each list. But it’s an amazing shift. More than 1 in 20 girls born in 1914 was named Mary (the most popular name that year – and many other years too if you’re interested). By 2014, just over 1 in 100 girls born were given the most popular name that year, “Emma.” This is part of a larger trend in naming practices–popular names just aren’t as popular as they used to be. Stanley Lieberson refers to this as the “modernization theory” of name trends. The theory suggests that as institutional pressures associated with names decline (e.g., extended family rituals, religious rules), we see the proliferation of more diverse names. But there’s a twist. The phenomenon is also gendered: popular boy names have always been more popular (in aggregate) that popular girl names. Below, I’ve charted the proportion of boys and girls born in the U.S. with top 10 names from 1880-2014. Boys given top ten names in 1880, for instance, accounted for more than 40% of all boys born. And the most popular boy names have always accounted for a larger share of all boys born than the most popular girl names for girls born. It’s not a new fact and I’m not the first to notice it. (Though, as you can see below, the lines have just recently met, and they could conceivably cross paths any year now. And that will be something that has never happened.)Baby Name FrequenciesIn 1965, Alice Rossi suggested that part of what accounts for the discrepancy is related to gender inequality. As she put it, “Men are the symbolic carriers of the temporal continuity of the family” (here). Lieberson and Eleanor Bell later discovered that girls are more likely to have unique names as well (here). It’s an interesting example of something that many people teach in courses on men and masculinities. While men are, as a group, systematically advantaged, they may be held accountable to a more narrow range of gender performances than are women. And while men’s rights groups might frame this as an illustration of women being the group to benefit from gender inequality, it’s much better understood as what Michael Messner refers to as a “cost of privilege.”

Yet, this appears to be one costs of privilege that has decreased. In 1880, the top 10 boy names accounted for 41.26% of all boys born that year; the top 10 girl names accounted for 22.98%. There was more than an 18% gap. While boys’ popular names are still more popular than girls’ popular names, the gap shrunk to 0.27% by 2014. That’s a monumental shift. And I’m sure the modernization theory of name trends accounts for the lion’s share of the more general shift toward more secular names and a general decrease in name continuity between fathers and sons. But there is more than one way to read this shift. We might also say that this is a really simple illustration of one way that patriarchal family traditions have been chipped away over the past 100 years. Lots of data would support this conclusion.  We might account for it alongside, for instance, data showing the prevalence of women taking men’s surnames after marriage as a percentage of all marriages in a given year or opinions about surname change.  But it’s also an illustration of the ways that this process has meant changes for boys and men as well.

Masculinity has, quite literally, opened up. It’s something that has happened more for some racial and class groups than others. And whether this transformation–this “opening up”–is a sign of gender inequality being successfully challenged or reproduced in new and less easily recognizable ways is the subject of my favorite corner of the field.

 

 

Thinking Sociologically about #OscarsSoWhite: Measuring Inequality in Hollywood

Originally posted at Feminist Reflections

1498787_10202083647508448_647008496_oThe 2016 Oscar nominations were just announced.  This is the second year in a row that all 20 acting nominees are white–prompting the hashtag #OscarsSoWhite.  Matthew Hughey wrote on this issue last year as well.  The announcement got me thinking about inequality in film.  The nominees are selected by just over 7,000 members of the Academy of Motion Picture Arts and Sciences–so they are elected by a panel of peers.  But members of the AMPAS are not automatically voting members.  You have to apply, and your application has to be sponsored by existing member of the branch of the Academy for which you would like to be considered (here).  So, while the Oscars are awarded by a panel of peers, who make up the list of people who qualify as “peers” in the first place is a political matter.  And just like anywhere else, knowing someone who knows someone likely plays a role in gaining access.

Sociologists who study networks are often interested in how social networks provide access to various things people might want to acquire (wealth, status, access, “success” more generally, etc.).  This is why we have a concept for just how networked you are: “social capital.”  And certainly lots of people are complaining about the fact that Hollywood is an old, white, boy’s club and attempting to change this.  Indeed, Genna Davis founded an institute to study gender in the media.  April Reign (an editor at Broadway Black and NU Tribe Magazine) founded the hashtag #OscarsSoWhite after the all-white slate of nominees were announced last year.  And Maureen Dowd of The New York Times wrote an extensive article last year on the entrenched sexism that keeps women from occupying central roles in Hollywood.  Jessica Piven, one of directors quoted in the article, said:

“I feel that there is something going on underneath all of this which is the idea that women aren’t quite as interesting as men. That men have heroic lives, do heroic things, are these kind of warriors in the world, and that women have a certain set of rooms that they have to operate in.”

This belief system results in a network saturated with men and with precious few opportunities for women–and even fewer for women of color.  And as Effie Brown’s interaction with Matt Damon in “Project Greenlight” brought up, conversations about challenging the lack of diversity in Hollywood (similar to challenging the lack of diversity elsewhere) are often met with the presumption that diversity means compromising on ability, talent and creativity.  Entrenched sexism and inequality is a struggle to challenge in any institution because… well, because it’s entrenched.  So, it’s easy to feel like the most qualified guy who just happens to also be white without fully appreciating the fact that being a white guy might have been a big part of what gave you a foot in the door in the first place.

To think about this empirically, consider the party game “Six Degrees of Kevin Bacon”. The idea plays on the theory of “six degrees of separation”—part of a sociological puzzle called the “small world problem” asking just how connected everyone in the world is to everyone else.  The theory suggests that we are no more than six connections away from anyone in the world. In the early 1990s, some students at Albright University came up with the idea for the game: pick any actor and see if you can connect that actor with Kevin Bacon through shared movie appearances with other actors as the connections. Take Angela Bassett for example. Angela Bassett was in Sunshine State (2002) with Charlayne Woodard who was in He Said, She Said (1991) with… Kevin Bacon. So, Angela Bassett has a Bacon number of 2.Screen Shot 2016-01-15 at 4.18.58 PM

Later, a group of computer science students at the University of Virginia produced the network of actors to see how “central” Kevin Bacon actually is using IMDB.com (you can play around with the network on their site, www.oracleofbacon.org). And, as it turns out, Kevin Bacon is a central actor—he’s been in films with over 3,000 other actors and more than 99% of all of the almost 2 million actors listed on IMDB.com can be connected with Kevin Bacon in 5 connections or less. But, he’s not the most central actor. He’s actually the 411th most centrally connected actor (you can see the top 1,000 most “central” actors here). But, Kevin Bacon does share some things in common with the most central actor (Eric Roberts): they’re both white, they’re both men, and they were both born within two years of each other.  Coincidence?

When I encountered the list, I noticed that there weren’t many women. There are only 3 in the top 100 most central actors.  And all three are white.  So, I wrote a script to data mine some basic information on the top 1,000 to see who they are using data from IMDB.com (birth year) as well as NNDB.com (which lists race and gender).*  The list, perhaps unsurprisingly, is dominated by men (81.75%) and by white people (87.8%). Below is the breakdown for proportions of actors among the top 1,000 most central actor by gender and race.IMDB - Gender and RaceIt’s a powerful way of saying that Hollywood continues to be a (white) boy’s club. But they’re also an old white boy’s club as well. I also collected data on birth year. And while the 50’s were the best decade to be born in if you want to be among the 1,000 most “central” actors today, the data for the men skews a bit older.** This lends support to the claim that men do not struggle to find roles as much as women do as they age–which may also support the claim that there are more complex roles available to men (as a group) than women.IMDB Birth Year - MenIMDB Birth Year - WomenThe other things I noticed quickly were that: (1) Hispanic and Asian men among the top 1,000 actors list are extremely likely to be typecast as racial stereotypes, and (2) there are more multiracial women among the top 1,000 actors than either Hispanic or Asian women.

Part of what this tells us is that we like to watch movies about white people and men… white men mostly.  But part of why we like these movies is that these are the movies in which people are investing and that get produced.  As a result of this, there are a critical mass of super-connected white men in Hollywood.  So, it shouldn’t surprise us that white actors dominate the Oscar nominations. They’ve been hoarding social capital in the industry since it began.  #OscarsSoWhite

_________________________________

* To get the data, I wrote a Python script using the Unofficial IMDb API and the NNDB.com’s API. The results were able to read data for gender for all 1,000 people on the list but only gender, birth year, and race for 959 of the 1,000 people in the dataset. The other 41 had incomplete information on both sites. And I didn’t bother to clean the data up any more.

**Part of becoming a more central actor in the network of all actors has to do with having been a part of a mass of filmed projects with a variety of different actors.  The most central actor – Eric Roberts – has worked on projects with more than 8,000 other actors over the course of his career.  And being alive longer (perhaps obviously) helps.  But, it’s not all older actors.  And you don’t have to be living to be on this list.  But, actors born in the 1920’s, 30’s, and 40’s aren’t as central (as decade-based groups).  So, some of this is also having been in your 20’s, 30’s and 40’s between 1970 and 1990 which was a big period of growth for Hollywood.

What Constitutes a Mass Shooting and Why You Should Care

What Constitutes a Mass Shooting and Why You Should Care

By: Tristan Bridges, Tara Leigh Tober, and Nicole Wheeler

Originally posted at Feminist Reflections.

How many mass shootings occurred in the United States in 2015? It seems like a relatively simple question; it sounds like just a matter of counting them. Yet, it is challenging to answer for two separate reasons: one is related to how we define mass shootings and the other to reliable sources of data on mass shootings.  And neither of these challenges have easy solutions.

As scholars and teachers, we need to think about the kinds of events we should and should not include when we make claims about mass shootings.  Earlier this year, we posted a gendered analysis of the rise of mass shootings in the U.S. relying the Mass Shootings in America database produced by the Stanford Geospatial Center. That dataset shows an incredible increase in mass shootings in 2015. Through June of 2015, we showed that there were 43 mass shootings in the U.S. The next closest year in terms of number of mass shootings was 2014, which had 16 (see graph below).  That particular dataset relies heavily on mass shootings that achieve a good deal of media attention.  So, it’s possible that the increase is due to an increase in reporting on mass shootings, rather than an increase in the actual number of mass shootings that occurred.  Though, if and which mass shootings are receiving more media attention are certainly valid questions as well.

Mass Shootings (Stanford) 1If you’ve been following the news on mass shootings, you may have noticed that the Washington Post has repeatedly reported that there have been more mass shootings than days in 2015. That claim relies on a different dataset produced by ShootingTracker.com. And both the Stanford Geospatial Center dataset and ShootingTracker.com data differ from the report on mass shootings regularly updated by Mother Jones.* For instance, below are the figures from ShootingTracker.com for the years 2013-2015.

Mass Shootings, 2013-2015 (ShootingTracker.com)1For a detailed day-by-day visualization of the mass shootings collected in the ShootingTracker.com dataset between 2013 and 2015, see below (click each graph to enlarge).

Mass Shootings 2013

Mass Shootings 2014

Mass Shootings 2015

 

The reason for this discrepancy has to do with definition in addition to data collection.  The dataset produced by the Stanford Geospatial Center is not necessarily exhaustive.  But they also rely on different definitions to decide what qualifies as a “mass shooting” in the first place.

The Stanford Geospatial Center’s Mass Shootings in America database defines mass shootings as shooting incidents that are not identifiably gang- or drug-related with 3 or more shooting victims (not necessarily fatalities) not including the shooter.  The dramatic spike apparent in this dataset in 2015 is likely exaggerated due to online media and increased reporting on mass shootings in recent years.  ShootingTracker.com claims to ensure a more exhaustive sample (if over a shorter period of time).  These data include any incidents in which four or more people are shot and/or killed at the same general time and location.  Thus, some data do not include drug and gang related shootings or cases of domestic violence, while others do.  What is important to note is that neither dataset requires that a certain number of people is actually killed.  And this differs in important ways from how the FBI has counted these events.

Neither ShootingTracker.com nor the Stanford Geospatial Center dataset rely on the definition of mass shootings used by the Federal Bureau of Investigation’s Supplementary Homicide Reporting (SHR) program which tracks the number of mass shooting incidents involving at least four fatalities (not including the shooter). The table below indicates how different types of gun-related homicides are labeled by the FBI.

Screen Shot 2015-12-14 at 2.05.18 PMOften, the media report on events that involve a lot of shooting, but fail to qualify as “mass murders” or “spree killings” by the FBI’s definition.  Some scholarship has suggested that we stick with the objective definition supplied by the Federal Bureau of Investigation.  And when we do that, whether mass shootings are on the rise or not becomes less easy to say.  Some scholars suggest that they are not on the rise, while others suggest that they are.  And both of these perspectives, in addition to others, influence the media.

One way of looking at this issue is asking, “Who’s right?”  Which of these various ways of measuring mass shootings, in other words, is the most accurate?  This is, we think, the wrong question to be asking.  What is more likely true is that we’ll gather different kinds of information with different definitions – and that is an important realization, and one that ought to be taken more seriously.  For instance, does the racial and ethnic breakdown of shooters look similar or different with different definitions?  No matter which definition you use, men between the ages of 20 and 40 are almost the entire dataset.  We also know less than we should about the profiles of the victims (those injured and killed).  And we know even less about how those profiles might change as we adopt different definitions of the problem we’re measuring.

There is some recognition of this fact as, in 2013, President Obama signed the Investigative Assistance for Violent Crimes Act into law, granting the attorney general authority to study mass killings and attempted mass killings.  The result was the production of an FBI study of “active shooting incidents” between 2000 and 2013 in the U.S.  The study defines active shooting incidents as:

“an individual actively engaged in killing or attempting to kill people in a confined and populated area.” Implicit in this definition is that the subject’s criminal actions involve the use of firearms. (here: 5)

The study discovered 160 incidents between 2000 and 2013.  And, unlike mass murders (events shown to be relatively stable over the past 40 years), this study showed active shooter incidents to be on the rise.  This study is important as it helps to illustrate that the ways we have operationalized mass shootings in the past are keeping us from understanding all that we might be able to about them.  The graph below charts the numbers of incidents documented by some of the different datasets used to study mass shootings.

Mass Shootings Comparison

Fox and DeLateur suggested that it is a myth that mass shootings are on the rise using data collected by the FBI Supplementary Homicide Report.  We added a trendline to that particular dataset on the graph to illustrate that even with what is likely the most narrow definition (in terms of deaths), the absolute number of mass shootings appears to be on the rise. We do not include the ShootingTracker.com data here as those rates are so much higher that it renders much of what we can see on this graph invisible.  What is also less known is what kind of overlap there is between these different sources of data.

All of this is to say that when you hear someone say that mass shootings are on the rise, they are probably right.  But just how right they are is a matter of data and definition.  And we need to be more transparent about the limits of both.

_____________________

*Mother Jones defines mass shootings as single incidents that take place in a public setting focusing on cases in which a lone shooter acted with the apparent goal of committing indiscriminate mass murder and in which at least four people were killed (other than the shooter).  Thus, the Mother Jones dataset does not include gang violence, armed robbery, drug violence or domestic violence cases.  Some have suggested that not all of shootings they include are consistent with their definition (like Columbine or San Bernardino, both of which had more than one shooter).