Urbanicity and LGBT Demographics

As more and more surveys are including questions attempting to capture the size of the LGBT identifying population in the U.S., I’ve been interested. I’m interested in the measures being used, what they’re able to capture, how those estimates vary when we compare surveys, and the specific wording of questions used.

In the past few years, Gallup’s estimates (after partnering with The Williams Institute and Gary Gates) have received an incredible amount of attention. Reporting on the most recent wave of data collected, Gallup reported that 4.5% of the U.S. population identifies as LGBT. It’s a bold claim. And, as with most estimates, it is most likely a conservative estimate.

Because of how Gallup asks this question, we cannot disaggregate lesbians, gay men, bisexuals, and transgender folks from one another. They’re just lumped together. And much of the reason for this is… wait for it… probably best blamed on straight folks. Why? Designing survey questions that can reliably and accurately assess sexual identities is challenging for lots of reasons I’ve discussed before. But one reason worth noting is that straight folks – heterosexual people – are among the biggest hurdles. Enough heterosexual people cannot make sense of questions inquiring about their sexual identities that we worry about “false positives” (straight people not knowing how to answer and responding that they are “bisexual” or “lesbian” or anything other than straight not because they identify that way, but because they don’t understand the question). Read (or listen to) how sexual demographer Gary Gates puts in when talking about the simple question Gallup has asked survey respondents in two waves now:

“It’s a simple yes or no answer. One of the challenges that we’ve observed in measuring sexual orientation, and this may sound humorous to people, but heterosexuals often don’t know what their sexual orientation is and don’t routinely call themselves either heterosexual or straight. And so when you have questions where you’re asking people what they are, that very big population sometimes makes mistakes and it creates what we call ‘measurement error’ or ‘false positives.’ And it basically puts people in the LGB category that really aren’t…. With the Gallup question, you’re not asking that group what they are, you’re asking what they aren’t. And they more or less know that. So they may not use terms like heterosexual or straight. But they know they’re not gay.”

-Gary Gates (HERE)

In the Gallup Poll, respondents are asked, “Do you personally identify as lesbian, gay, bisexual, or transgender?” The responses are a simple yes/no. And all because straight folks and cisgender folks are so likely to misunderstand the question and inaccurately report their sexuality (and possibly gender).

So, it’s a conservative estimate that doesn’t allow us to break the LGBT population apart as much as we might like if we’re interested in understanding where growth in the population is and isn’t happening. But, because Gallup collects such a large sample, they are able to report on state-level estimates of LGBT populations throughout the U.S. I’ve written about this before. I updated a figure I previously produced for a lecture I’m giving and thought I’d share in case it’s useful to others as well.

We know that states with larger shares of the population living in urban areas have higher proportions of LGBT identifying individuals. There is more than one hypothesis about why this is or might be the case. I’ve charted these data before, but I added a new element to the figure below. Now it charts proportion identifying as LGBT by state by proportion of the state population living in urban areas AND data points vary by size according to the size of state populations relative to one another.

Personally, I’m excited to see the 2017-2018 data (which I imagine might be released soon) because from the work I’ve been reading, a great deal of growth in LGBT-identifying population is happening in the South in the U.S. And on the figure above, few southern states are above one standard deviation above the trend line (with the exception of Georgia). I wonder what this figure will look like when we map the population with more recent data. I have a feeling things have changed and I’m interested to see how.

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Special thanks to Connor Gilroy for answering my RStats question on Twitter, enabling me to figure out the code.

Race, Education, Running and Spatial Inequalities

A while ago, I wrote a post at Sociological Images looking at the ways Dustin A. Cable’s map visualizing racial segregation in the U.S. compared with Kyle Walker’s map examining educational segregation in the U.S. My interest was in was in examining spatial inequality. In a nutshell, where you live matters. It plays an important role in what kinds of resources you have access to (or don’t). It shapes your future earnings, how much education you’re likely to receive (in addition to the quality of that education), how long you live, and much more.

I was interested in putting the two maps into conversation because sociologists who study inequality are interested in a specific social process wherein privilege and inequality tend to accumulate. That is, some kinds and qualities of resources (economic, social, cultural) are found in abundance in some contexts, to a lesser extent in others, and are virtually absent in many places. And you can see these accumulations. Non-white populations (specifically Black and Hispanic) are in high concentrations on the maps in the same areas lower levels of education are present.

Last week, Runner’s World ran a story that looks at where people run according to Strava’s maps to talk about racial segregation and it reminded me of this post. I went through and looked up some of the big cities to see how where most popular routes are for runners using Strava. [Note: I run and do not use Strava, and many runners I know do not either. So, this is not a measure of exercise in a given area; it’s a measure of how many people exercise using Strava.]

If you’re unfamiliar, Strava is a mobile app and website on which runners and cyclists are able to post their exercise and connect with others doing the same. So, it’s basically a facebook page for people who exercise and want to log their miles to go online and hold themselves and each other accountable (and probably to brag a little too).

But look at how racial segregation, educational segregation and Strava use map onto one another! Below, is the map I created so that you can see San Francisco, Berkeley, and San Jose, California in the same frame using Walker’s map of educational attainment (top) over Cable’s racial dot map (bottom). [Note the legends on each map when deciphering the meanings.]

Here’s where people run using Strava in the same areas:

Here’s Chicago:

And Chicago’s Strava runners:

Los Angeles:

And L.A.’s Strava enthusiasts:

New York City:

NYC Strava users:

Detroit, Michigan:

Detroit Strava users:

and, finally, Houston, Texas:

And the Strava scene in Houston:

This could be an indication that white people are exercising more than are other groups (or, more accurately, that white people who exercise are simply a lot more likely to turn to Strava to tell everyone how much they exercised and where than are other racial groups). But it is also a kind of social network that people living in predominantly white areas seem to be tapped into that other groups are less likely to use (and part of this is both knowing about the Strava community and believing it would be worthwhile to join). It’s a social network white people seem to be using more than other groups. And that makes me wonder what else they might be getting from logging their extracurricular exercise on social media.

But it also made me think of Rashawn Ray‘s research on how varying racial compositions of neighborhoods influences Black men and women to engage in more of less physical activity in their leisure time. And it could provide a different kind of evidence for spatially-based health inequalities.

Segregation is one powerful way that inequalities persist–its also a way that many are kept blithely unaware of the existence of stark social inequalities. It can be hard to notice inequality when we’re segregated from each other (by race, class, education). But mapping where we run offers a powerful illustration of some of these inequalities – and the use of Strava is one small piece of how inequalities are sustained.

Google, Tell Me. Is My Son Gay?

Originally posted at Feminist Reflections.

In 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 boys’ 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).The 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.

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 Enduring Feminist Sociology of Joan Acker

Joan Acker recently passed away.  I read the news on Twitter—someone in my news feed shared, “The world lost a giant.”  It’s true.  Her scholarship was titanic.  Acker quite literally altered the way we understand gender and provided a framework for understanding the ways gender becomes embedded in social structures and institutions that we have all been relying on ever since.  Joan Acker is my favorite kind of sociologist—she questioned something the rest of us had been under the assumption was unquestionable.  As the sociologist Jurgen Habermas wrote, “It takes an earthquake to make us aware that we had regarded the ground on which we stand everyday as unshakable.”  Joan Acker shook the very ground upon which sociologists of gender stood in this sense.  She questioned the unquestionable in the best of all ways.  She lay bare a theory and method of understanding gender inequality that helped us better understand just how pernicious it is.

Acker’s theory never gained the same kind of popularity associated with West and Zimmerman’s interactional theory of gender.  But we all rely on Acker.  When we refer to formal and informal collections of jobs, people, and organizations as “gendered,” we’re relying on her work.  Society is organized in ways that cause some people to experience a more seamless “fit” in some positions than others.  Stay-at-home fathers have a unique set of struggles associated with lacking a clear “fit” in similar ways to women who occupy jobs in the upper echelons of organizations dominated by men.  Society is organized in ways that cause us to experience this.

Acker labeled this and theorized a language to study it and shine some much-needed light and attention on the ways that gender difference and inequality are part of the very structure of society at a fundamental level. Acker’s most cited and celebrated publication was published in Gender & Society in 1990: “Jobs, Hierarchies, Bodies: A Theory of Gendered Organizations.”  In it she begins:

“Most of us spend most of our days in work organizations that are almost always dominated by men.  The most powerful organizational positions are almost entirely occupied my men, with the exception of the occasional biological female who acts as a social man.  Power at the national and world level is located in all-male enclaves at the pinnacle of large state and economic organizations.  These facts are not news, although sociologists paid no attention to them until feminism came along to point out the problematic nature of the obvious.  Writers on organizations and organizational theory now include some consideration of women and gender, but their treatment is usually cursory, and male domination is, on the whole, not analyzed and not explained.”

Building on many other feminist scholars (including Heidi Hartmann, Rosabeth Moss Kanter, Dorothy Smith, and more), Acker helped to show how gender differences in organizational behavior and outcomes were best explained by structural and organizational characteristics.  Gender difference was/is embedded in organizational structure, and Acker designed a language and theory for examining just what it means to consider gender inequality as “institutionalized.”

Within the logic of organizations, jobs are technically open to anyone; and they are stratified by complexity and responsibility.  This is how we create workplace hierarchies.  And they feel gender neutral.  Acker questioned this assumption.  Abstract jobs have the appearance of gender neutrality until we try to take a concrete example which necessitates something else—an ideal worker.

“Such a hypothetical worker cannot have other imperatives of existence that impinge upon the job…  Too many obligations outside the boundaries of the job would make a worker unsuited for the position.  The closet the disembodied worker doing the abstract job comes to a real worker is the male worker whose life centers on his full-time, life-long job, while his wife or another woman takes care of his personal needs and his children…  The concept of ‘a job’ is thus implicitly a gendered concept, even though organizational logic presents it as gender neutral.”

These are, today, routine assumptions from which scholars of gender from a range of disciplines proceed to study gender and inequality.  But they weren’t when Joan Acker was studying.  Acker’s theorization of institutionalized forms of inequality is a dominant theoretical perspective in the sociology of gender today.  At the conclusion of her article, she theorizes what it would take to dissolve the institutionalized forms of inequality in organizations.

“Such a transformation would be radical in practice because it would probably require the end of organizations as they exist today, along with a redefinition of work and work relations.  The rhythm and timing of work would be adapted to the rhythms of life outside of work.  Caring work would be just as important and well rewarded as any other; having a baby or taking care of a sick mother would be as valued as making an automobile or designing computer software.  Hierarchy would be abolished, and workers would run things themselves.  Of course, women and men would share equally in different kinds of work.  Perhaps there would be some communal or collective form of organization where work and intimate relations are closely related, children learn in places close to working adults, and workmates, lovers, and friends are all part of the same group.”

Like much of the structural theory of gender—particularly that work being published in the late 80s and early 90s—Acker proceeds from an unapologetically Marxist orientation.  And while we continue to study gender inequality from Acker’s vantage point, less has been done toward her vision of social transformation than she might have imagined would be when she published this a quarter century ago.  It still sounds so radical listed out above.  But is it really so radical a notion?  She concluded that article with a simple point.  We can organize society differently, in ways that continue to ensure that what needs doing gets done without all of the dominance, control, and subordination currently connected with these tasks.  The battles will always be fought over what actually comprises the “what needs doing.”  But Acker’s proposal for what needs doing is beautiful in its simplicity: “producing goods, caring for people, disposing of the garbage.”  Why any of the three of those should be considered more important than the rest is something we should continue to question.

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:

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):

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.

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

Thinking Sociologically about #OscarsSoWhite: Measuring Inequality in Hollywood

Originally posted at Feminist Reflections

The 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.

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 411^th 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.It’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.The 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

This 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.  We 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

• What Constitutes a Mass Shooting and Why You Should Care (Tristan Bridges, Tara Leigh Tober, and Nicole Wheeler)
• Studying Race and Gender in Comic Books with Color Codes (Tristan Bridges)
• Cosmopolitan Masculinities and Gender Omnivorousness: Transformations in Gender and Inequality (Tristan Bridges)
• Bronies, Anti-Rape Chants, and Gendered Change (C.J. Pascoe and Tristan Bridges)
• Drawing Race and Class Boundaries with Sexual Discourses (Tristan Bridges)

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

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.

If 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.

For 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).

 

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.

Often, 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.

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).

Trends in Masculinity on Reddit

Originally posted at Girl W/ Pen!

Reddit is a website for sharing links and commenting on them.  You may not have heard of it, but it’s might be more popular than you think.  In November of 2015, Reddit received just shy of 200,000,000 unique visitors from 215 countries viewing a total of more than 7 billion pages on the site.  In the U.S., it ranks as 1 of the top 10 visited sites.  So, it’s a massive undertaking and the site receives an incredible amount of online traffic.  And users don’t comment on every link shared and some certainly just view conversation threads without commenting.  But there are close to 2 billion comments on the site as well.  And those comments are chock full of internet slang, and all manner of online vernacular.  Recently, Randal S. Olson partnered with FiveThirtyEight.com to produce a n-gram viewer for Reddit comments similar to the Google n-gram viewer introduced in 2010. The tool allows you to search for 1, 2, or 3-word phrases and to see their prevalence among all n-grams between 2007 and August of 2015.

But, it’s important to note that although Reddit has an extremely large audience and readership, the tool does not provide a representation of how all people communicate online.  Rather, it represents how Reddit users communicate with each other online.  So, who, you might ask, are Reddit users.  According to Google Display Planner and FiveThirtyEight, Reddit users are almost entirely 35 or younger and around 80% are men.  And Reddit has a reputation for being a racist, sexist, homophobic, and generally anti-woman online space.  So, it does give us an interesting peek at trends within one popular online hangout.

For instance, below you can see the prevalence of “dude” and “bro” among Reddit comments.  Both have become more popular over time.  I don’t know what it means that they’re more common, but it’s interesting to see.

Similarly, “no homo,” “fag” and “faggot” enjoy a healthy portion of Reddit comments. And we can track trends in the recent spate of masculinity-related portmanteaus connecting masculinity with all manner of socially undesirable behavior–like “mansplaining” and “manspreading” (below).

What these trends mean is a different question and not one these data can answer.  But it is an interesting way of tracking trends among this group of primarily young men online.

Twitter Activity at the American Sociological Association Summer 2015 Meeting

The 2015 Summer Meeting of the American Sociological Association was held this past weekend in Chicago.  It’s a conference primarily dedicated to members of the organization.  But, reporters, editors, all manner of professionals in the publishing industry, and more are there as well.  For the last few years, I’ve been watching the meetings happen digitally while they occur in “real” time and space as well.  Friday through Monday had the most action on twitter.

But, the digital ASA isn’t just about tweet volume.  Twitter also has a wealth of data on relationships between various Twitter users.  Below, I produced a network diagram that illustrates all of the tweets that used the #ASA15 hashtag during the conference.  I colored the nodes to illustrate groupings of Twitter users and scaled nodes (and labels) for size based on how important a given node was within the network.  This allows us to see not just who tweeted, but whose tweets were most interacted with.

click to enlarge

Others have produced diagrams like these and I’ve always found them captivating.  But, some elements are lost in them as well.  For instance, we can’t tell time order.  The tweets sort of appear to have happened simultaneously in the diagram above.  It’s a post-game analysis.  And the big players stand out – see below. (Spoiler: I’m not one of them).

click to enlarge

But even this doesn’t actually allow us to see how this complex network emerged in real time.  The timelapse map below shows the tweet volume we see on the graph at the top and also georeferences the tweets to tell us where the Twitter users were when they were tweeting.  You can see the tweeting die down at night and start up again the next day as well.

It is fascinating to see just how much national and international participation there was.  Initially, I imagined that folks might start out in different corners of U.S. and abroad, but that most of that activity would collapse into Chicago during the weekend of the conference.  But, there’s a lot of digital participation from folks who didn’t attend.  I only live tweeted one session.  But I was thanked by a few folks who were listening in from far away.  I’ve never thought of Twitter as public scholarship in this way before.  But, part of what we are doing when we tweet at conferences is helping to open up those ideas and networks to others (scholars unable to attend, students, journalists, and more).  I’m still getting used to using Twitter at conferences.  But, I’m newly convinced that it’s worth the effort.