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.

The Lesbian Wage Premium

By: C.J. Pascoe and Tristan Bridges

C.J. and her public school teacher female partner have had some version of the following conversation with faculty wives (those married to men) countless times over the past decade:

Wives: “Wow, my husband just works so hard. It’s like I’m a single parent. But academia’s just like that –totally unpredictable. He has to work evenings and weekends to get published and travel all the time to conferences. I have to not work/adjust my schedule/work part time to make sure child care is covered/food is made/house is taken care of.”

C.J.’s public school teacher partner: “Huh. That doesn’t sound like C.J.’s schedule at all. She works 9-5 and we share childcare equally. She does some work after they go to bed and during naptime (and let’s be honest between the 4 am and 6 am wakeups in those early days), but we have a fairly regular schedule and division of labor. FullSizeRenderShe grocery shops, I take the kids to dance classes while she does so. She puts the kids to bed, I clean the house. Mornings are evenly divided between the two of us (though we do make the kids stay in bed until 6:20 so we can both get in early morning workouts!). Sure there are evening events/conferences/invited talks, but we plan those out in advance to make sure each of our jobs are covered. In fact when C.J. travels the table is covered in Tupperware and prepared meals so she holds up her part of the labor before she leaves (see image). Weird, it’s like our partners work in two totally different industries.”

Over and over and over again. So it was with only a little surprise that I read this headline in the Washington Post: “The Surprising Reason Why Lesbians Get Paid More Than Straight Women.” It turns out Marieka Klawitter, professor of public policy, examined 29 studies “on wages and sexual orientation and found a 9 percent earnings premium for lesbians over heterosexual women.” She suggested that this premium was due to lesbians’ increased levels of education and work experience.

Another another recent study, the article goes on to point out, showed that lesbians who had previously lived with a male partner made 20% less than those who never had lived with a man (though even these lesbians still made more than heterosexual women who lived with a male partner). Indeed, this “male partner penalty” reflects what Philip Cohen points out in this graph about women’s median earnings as a proportion of men’s by education (below). You can see the increase in salary proportionally for those who have not only never had kids, but are also not married.

Graph produced by Philip Cohen - https://familyinequality.wordpress.com/2013/11/08/gender-gap-statistic-gets-it-from-all-sides/
Graph produced by Philip Cohen –
https://familyinequality.wordpress.com/2013/11/08/gender-gap-statistic-gets-it-from-all-sides/

So what is going on here? We, in consultation with Facebook friends, have a few ideas:

  1. See the conversation above – that perhaps the premium is reflecting the fact that women in same-sex couples don’t perform a full second shift and perhaps engage in a more equitable division of labor. Time is not valued or undervalued by gender, in other words.
  2. Women’s work success may threaten their heterosexual relationship and they may reduce their professional efforts. This reduction is reflected in salary. Certainly research by Christin Munsch on women’s earnings and cheating patterns suggests that women’s earning power may not positively affect heterosexual relationships. (Idea credit: Kate Howlett McCarley)
  3. The wage premium has nothing to do with lesbians and everything to do with whether or not a woman lives with a man. We might see something similar for single straight identified women. (Idea credit: Siri Colom)
  4. The living with a man penalty might reflect regional patterns of homophobia and be less about the man himself. (Idea Credit: Megan Carroll)
  5. This might have something to do with queer gendered embodiments in the workplace. As Jane Ward asked “did they control for butchness?” Or Terri Eagen-Torkko suggested (tongue perhaps in cheek): “It’s probably just the half of us who are ‘the man.’” Indeed, could it be that there is something about the way one “does gender” that is different when one is lesbian identified? So lesbian identified that one has never lived with a man? More assertive perhaps? As such less prone to the mistakes women are told they make in negotiating salaries?
  6. Finally, these findings need to be squared with the recent study that showed that women who might be read as queer because of their work experience are less likely to be called by prospective employers in the first place. (Idea credit: Dawne Moon and Sascha Demerjian)

It’s likely a combination of all of these factors and more. But given the difference male partners make in the equation, we can’t shake the notion that domestic division of labor plays a big role here. And while those of us in same-sex couples may be freer to create new scripts for these duties, as Tristan can attest, it’s challenging, but can be done, in heterosexual relationships, too.

In heterosexual relationships, the script is institutionalized such that deviating from it is challenging for many reasons beyond people feeling like “less of a man” or as though they are failing to live up to motherhood ideals. While actually measuring an equitable division of labor is challenging in any relationship, there are social forces working against heterosexual couples attempting for forge egalitarian divisions of labor—perhaps particularly when they have children. Part of this might have to do with actual, authentic collaboration and support. The joys and burdens of relationships need to be balanced, and it’s probably not all that shocking to hear that lesbian couples might be better at this. Heterosexual relationship scripts are institutionalized in ways that make men and women unhappy (though, for very different reasons). Challenging these means forging new scripts—a march that is invariably uphill.

Indeed, we have learned to rely on one another as coauthors in this way as well—passing papers back and forth and trying to assess work/family balance issues, and more. It enriches our work lives. The labor for this blog post itself, in fact, was aided by a queer digital network of people interested in similar issues and ideas and eager to help. In the end, these studies seem to raise as many questions as they answer about sexuality, gender, and the wage gap. And we ought to consider the questions posed as well as those that appear to be answered.

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.”

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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.

 

 

#HerWork2 – Acknowledging and Accounting for the Gender Recognition Gap

By: Tristan Pascoe and C.J. Bridges*

Originally posted at Girl W/ Pen!

A PhD student of economics at Harvard—Heather Sarsons—generated quite a buzz with her working paper, “Gender Differences in Recognition for Group Work” (HERE for the paper, and HERE for Justin Wolfers’ summary of her research in TheUpshot). Sarsons looked at the careers of young economists recruited by top universities in the U.S. over the past four decades. She discovered that while women publish at roughly the same rates as men, they are significantly less likely to achieve tenure, even after accounting for all the things one might first think to blame for this discrepancy (tenure rates at different universities, subfield differences, quality of publications, influence, etc.). There was one group of women, however, who received equivalent rates of success to men—women who publish without men, either alone or with other women. Simply put, Sarsons finds that when women publish with men, they do not receive the same credit.

Screen Shot 2016-02-03 at 11.53.03 AMBoth of us are sociologists. And, in Sarsons’ paper, she also analyzed sociology and did not find the same difference in terms of how men and women receive credit for collaboration. Economists list authors alphabetically on publications. Sociologists select author order on the publication. Thus, we have publications listed as “Bridges and Pascoe” as well as “Pascoe and Bridges.” We see each of these collaborations as equal partnerships, but have worked out a system for selecting first author that has to do with who manages the various projects on which we collaborate.

We also have a good working relationship in terms of giving each other credit, and for collaboratively taking credit for work that belongs more to “us” than to either of us individually. As we’ve theorized hybrid masculinities, for instance, we have tried to be careful to ensure that the framework is attributed to both of us. The initial publication came out of research Tristan published in Gender & Society—an article that benefited a great deal from C.J.’s reading and feedback. And we collectively realized that part of what Tristan had found was something lots of different scholars were finding. So, we collaborated on a paper for Sociology Compass that creates a more general framework for studying transformations in masculinity. Tristan was first author on that paper (though it was an equal collaboration) in part because C.J. was first author on our recent anthology, Exploring Masculinities (also an equal collaboration). We are currently at work on a separate theoretical article building on the framework we established a year ago and C.J. will be lead author on this. Author order has always been an easy conversation for us.  But we do talk and worry about whether there is or will be an discrepancy in the credit we each receive for the work.

Sometimes we perceive that Tristan receives more credit for our collaborations which may be due to the fact that he is a man. Sometimes we perceive that C.J. receives more credit for our collaborations because of her seniority and previous publishing record. We each attempt to negotiate these potential credit discrepancies differently, hoping to make up for something that might occur in our own collaboration relationship (despite Sarsons not finding it in sociology more generally). And, if we had a finer measure and found the gender credit gap in sociology, we admit that it would be something over which we have little control as individuals. But, as feminist sociologists who believe in the collaborative process, we decided to develop a list of feminist practices for cross gender collaborations.

10 Practices Men Who Collaborate with Women Should Consider

  1. ALWAYS acknowledge your coauthor whenever you discuss or write about the collaboration.
  2. Promote your coauthor’s solo-authored work and accomplishments.
  3. Consider very carefully if and when you are listed as lead author in your collaborations.
  4. Cite your coauthor’s solo-authored work.  #CiteHerWork
  5. When writing about or discussing the work, use “WE” and “OUR.”
  6. Acknowledge this bias when discussing, teaching, citing, other collaborations between women and men.
  7. Involve your coauthor in any attention, recognition, or opportunities that result from the collaboration.
  8. Whenever you can, discuss the work together and/or SHE speaks for US.
  9. Say something if and when you feel you’re receiving an undue proportion of the recognition.
  10. Understand that this issue is structural and you are not always aware of when and how you benefit.

This list is a work in progress and we would love to hear your additions!

#HerWork2

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*Deciding on author order for this post was simply not possible.

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

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* 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.

A Year in Review & Best of 2015 – Inequality by (Interior) Design Edition

Pascoe and Bridges - Exploring MasculinitiesThis was a big year.  My anthology with C.J. Pascoe was published last summer – Exploring Masculinities: Identity, Inequality, Continuity, & Change.  We’ve received really wonderful feedback so far and are completely thrilled to see that the book is provoking the sorts of conversation we hoped it might.  Recently, we discovered that the introduction to Exploring Masculinities was cited in Janice Yoder’s introductory editorial at Sex Roles after she took office.  Some of what we summarize in that introduction is how the sociology of masculinities grew out of a dissatisfaction with sex role theory in the 70’s and 80’s.  Screen Shot 2015-12-28 at 10.01.53 AMWe discuss the theoretical interventions that followed as attempting to make up for the shortcomings of sex role theory and a structural functionalist framework for understanding gender.  Yoder cites some of this in her editorial, “An Up-To-Date Gender Journal with an Outdated Name.”  My vote is that we actually change the name; I’d suggest “Gender Relations.”  I’ll explain more in an early post in 2016 (I promise).

C.J. and I are also excited that work on “hybrid masculinities” continues to provoke a great deal of interest as well.  We’re starting to see articles come out that rely on a framework I resurrected in my 2014 article in Gender & Society and summarized in more detail with C.J. Pascoe in Sociology Compass in 2014.  That the framework is proving useful in explaining findings across a diverse collection of studies provoked C.J. and I to pursue a more nuanced theorization (an article currently in progress) as well as an edited volume on hybrid masculinities (also in progress).

I’m also continuing to plug away at my book prospectus and finishing up a couple pieces as articles.  That’s been a challenging process.  But I’m beginning to wrap my head around it.

All of my favorite sociology blogs include year end reviews of their biggest and best posts.  While I’ve started to use this space as more of a digital archive to collect all of the blogging I do elsewhere, I thought it would be fun to share some of the most popular posts I wrote this year (along with some of my personal favorites).

Top 5 Most Popular Posts of 2015

  1. Masculinity and Mass Shootings in the U.S. (Tristan Bridges and Tara Leigh Tober)
  2. Beyond “Bossy” or “Brilliant”?: Gender Bias in Student Evaluations (Tristan Bridges, Kjerstin Gruys, Christin Munsch, and C.J. Pascoe)
  3. Pop Music, Rape Culture, and the Sexualization of Blurred Lines (Tristan Bridges and C.J. Pascoe)
  4. Bro-Porn Revisited: Heterosexualizing Straight White Men’s Anti-Homophobia (again) (C.J. Pascoe and Tristan Bridges)
  5. Twitter Activity at the American Sociological Association Summer 2015 Meeting (Tristan Bridges)

My Personal Favorites from 2015

In addition to these posts, I was also invited to write for CNN.com this year (on the masculinity of Donald Trump of all things) and a few of my posts were shared at Huffington Post and Pacific Standard (the latter, thanks to Sociological Images).

Perhaps most exciting (to me) of all is that a few of my posts (many coauthored) will be published in a forthcoming edited volume of short posts on gender – Assigned: Life with Gender – edited by Lisa Wade, Douglas Hartman, and Christopher Uggen (published as a part of The Society Pages Series published by W.W. Norton).  I’m enormously honored to be able to share some of this work in that volume and I can’t wait to see the finished product!

Additionally, I had my first blog post turn into a publication.  My colleague, Melody L. Boyd, and I had an idea for a review article about research on what sociologists call the “marriageability of men.”  We shared a post here (initially at Feminist Reflections) and wrote a review article that is forthcoming in Sociology Compass.

Finally, I continue to serve as a contributing editor at Feminist Reflections and to write a monthly column with C.J. Pascoe at Girl W/ Pen! –  Manly Musings.” C.J. and I regularly support guest posts at our column at Girl W/ Pen! and we are interested in continuing to support guest posts at Feminist Reflections as well.  It’s been incredibly fun and meaningful for me to have the opportunity to work with scholars who are blogging for the first time in addition to connecting with long-time bloggers to share their work and ideas on these two feminist scholarly digital spaces.  I’m looking forward to doing more of this in 2016.

Well… that about wraps up 2015.  Thanks for reading.

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.

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*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).