Sci-Hub users cost ASA journals thousands of downloads, and that’s OK

UPDATED to include Sci-Hub data from six months: September 2015–February 2016, and correcting a coding error that inflated download counts.

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Well, they might not have lost the downloads, but they didn’t get them.

Sci-Hub is a pirate operation that uses stolen university login credentials to harvest, store, and distribute for free virtually every academic article published anywhere. It is a simple, if criminal, solution to a very big problem: the lack of access to published research for people who can’t pay for it. When someone goes to the Sci-Hub site and requests an article, by simply pasting in the DOI or URL, the system either serves them the paper, or goes and steals it for them and then keeps a copy for the next user. For us university people who are used to dealing with the maze of logins and forwarding and proxies that come between us and the information we seek, it’s unbelievably fast and almost never fails.

Their most recent claim is an archive of 76 million papers and 400,000 users per day.

Today is 5 September and Sci-Hub is 8 years old. In eight years, the website grew from zero to 76,000,000 research articles available for free reading, and from 2,000 users per day to 400,000.

— Sci Hub (@Sci_Hub) September 5, 2019

Currently available at sci-hub.se or –.tw, it sometimes moves, but this site always lists where you can find it now. Naturally, both civil and criminal authorities are trying to shut it down, preferably by catching its mastermind, Alexandra Elbakyan, the elusive student programmer from Kazakhstan.

That picture is from the excellent (free streaming) documentary Paywall: The Business of Scholarship. Chris Bourg, the Director of Libraries at MIT (and a sociologist), also interviewed in the movie, said of Sci-Hub:

Those of us who work in scholarly communications, writ large, really have to look at Sci-Hub as sort of a poke in the side that says, “Do better.” We need to look to Sci-Hub to say, “What is it that we could be doing differently about the infrastructure that we developed to distribute journal articles, to distribute scholarship?” … I think we need to look at what’s happening with Sci-Hub, how it evolved, who’s using it, who’s accessing it, and let it be a lesson to us for what we should be doing differently.

Sociology’s stolen papers

Science magazine writer John Bohannon reached Elbakyan in 2016, and she turned over to him a 6-month cache of Sci-Hub server logs for a piece titled, “Who’s downloading pirated papers? Everyone.” He analyzed 28 million downloads, and Science made the data available for analysis, here. Eight million of those hits were from India and China, and the busiest location was Tehran.

The data archive includes only the time and date, the DOI number of each paper downloaded, and the location of the user. I’m not expert in DOI analysis, but Bohannon included a guide that shows the prefix 10.1177 is associated with Sage Publications, which publishes the American Sociological Association’s journals. Looking at the entire six-month series, September 2015 — February 2016, I found 171,000 Sage items, downloaded 377,000 times. Of those (if I got the DOIs right), 805 titles downloaded 1628 times came from the ASA research journals (my Stata code is here).

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ASA / Sage downloads from Sci-Hub, Sept 2015 – Feb 2016
	Articles	Downloads
American Sociological Review	239	693
Teaching Sociology	221	269
Journal of Health and Social Behavior	94	188
Social Psychology Quarterly	77	152
Sociology of Education	73	157
Sociological Methodology	57	76
Sociological Theory	44	93
Total	805	1628

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On an annualized basis, that would be 750,000 Sage downloads, and 3,200 from ASA journals specifically. For comparison, the most popular article in ASR in 2017 was downloaded about 10,000 times from the Sage site, so it’s a small share of the legitimate traffic. So over the life of Sci-Hub it cost (and saved) ASA thousands of downloads, probably a few tens of thousands. [Note in the first version of this post, I had a coding error that multiplied the counts, and this read “hundreds of thousands”. I regret the error.]

The most-downloaded ASR paper for the entire period was:

Mears, Ashley. 2015. “Working for Free in the VIP: Relational Work and the Production of Consent.” American Sociological Review 80 (6): 1099–1122. (downloaded 33 times)

The most-downloaded from a different journal was:

Kanazawa, Satoshi. 2010. “Why Liberals and Atheists Are More Intelligent:” Social Psychology Quarterly, February. (29 times)

I looked at a couple of them in more detail, and found, for example, that Paula England’s 2015 ASA Presidential Address was downloaded by users in Seoul (South Korea), Durban (South Africa), New Delhi, London, Chicago, Washington, and Virgie (Kentucky).

Interestingly, at least one of the popular papers, Lizardo et al.’s introduction to their editorial tenure at ASR, is already ungated on the Sage site, so you don’t need to use Sci-Hub to get it. This suggests, as Bohannon also noted, that some Sci-Hub users are just using the site because it’s convenient, not because they don’t have access to the papers.

Do you Sci-Hub?

I use Sci-Hub a lot, often for things that I also have subscription access to. (I do not, however, contribute anything to the system; I free-ride off their criminality.) Why? I’m not in the paywall game business, I’m in the information business. I am always behind on my work, and adding a few seconds or minutes of hunting for the legitimate way to get each of the many articles I look at every day is not worth it. (And when I find my university doesn’t subscribe? Interlibrary loan is wonderful, but I don’t want to spend more time with it than necessary.) Does my choice cost the American Sociological Association a few cents, by reducing legitimate downloads, which somehow factors into the profits that get kicked back to the association from Sage? I don’t know.

Of course, one of the dumb things about the paywall system is that it’s expensive and time-consuming to manage who has access to what information — it’s not a small task to keep information from reaching millions of determined readers from all around the world. (I assume one of the reasons my university recently introduced two-factor authentication — requiring me to click a pop-up on my phone every time I log in to university resources [even when I’m in my office] — is because of Sci-Hub. Ironic!)

Chris Bourg is right: “let it be a lesson to us for what we should be doing differently.” Elbakyan may have committed the most efficient product theft in history, in terms of list price of stolen goods per unit of effort or expense on her part. Her archive has been copied and distributed to different sites around the world (it fits in a large suitcase). And it was made possible by the irrational, corrupt nature of the scholarly communication infrastructure. Her success is the system’s failure.

For more information, read my report, “Scholarly Communication in Sociology.“

 

The blog’s decade

Blogging is dead. Long live the blog!

At 268,000, visits to this blog are now down 37% from the peak year of 2015. At the same time, this year I had the fewest number of new posts, just 39. On the other hand, this year I had 25 million impressions on Twitter. Whatever that means.

In my case, and probably many others, the role of the blog has changed with the growth of Twitter. A lot of what the blog did was provide an immediate outlet for daily chatter and work in progress thoughts, a way to get feedback, check in with colleagues, learn new things and meet new people. That’s a lot of what I use Twitter for now, more efficiently (if more noisily).

The other squeeze on the blog is the imperative to do open science more systematically, for which I use the Open Science Framework to post data and code — in projects, which may include multiple files, and quick files for single documents. And of course I use SocArXiv for more formal working papers, reviews, and preprints (mine are here).

So what is the role of the blog? It’s the place for official news and announcements about new work — including notifications of stuff I’m publishing elsewhere — longer arguments, and informal work. It’s a way for people to subscribe to my news via email (it also goes on Facebook, which a lot of sociologists use).

In several talks I have tried to illustrate the total information strategy in something like this pentagulation:

For a wider perspective, I also wrote a report on Scholarly Communication in Sociology, which is intended especially for grad students and early career scholars.

I’m happy to hear suggestions (on any platform) for how to handle communication strategy.

Book aside

The tricky relationship between platforms and different media came home to roost in my book, Enduring Bonds: Inequality, Marriage, Parenting, and Everything Else That Makes Families Great and Terrible. That book was inspired by the success of this blog, which is what enticed University of California Press to consider it. Unbeknownst to the vast majority of my readers on other platforms, I worked pretty hard on it, selecting the best blog posts, and then combining, updating, and adding to them to make a collection of essays, with data. I don’t know how successful the book is compared with other academic books generally, but, with almost no marketing beyond my social media platforms, it has generated basically no buzz for me (media, invitations, etc.). That’s in contrast to working papers, tweets, and blog posts, which continue to bring in wider attention. I know other people have done amazing blog-to-book projects, but this experience definitely showed me that the successful translation is far from automatic. Live and learn! Maybe in the long run the book will be what persists from the first decade of this blog.

The arriving divorce decline

In “The Coming Divorce Decline” I showed the U.S. divorce rate falling from 2008 to 2017, and predicted that, because the married population was being stocked with increasingly non-divorce-prone marriages, the rate would continue to fall. After the first draft (based on 2016 data), divorce fell in 2017, providing the first support for my prediction before the paper was even “published” (accepted for Socius). Now the 2018 data is out, and divorce has become less common still.

Here’s a quick update.

Based on the number of divorces reported in the survey each year, by sex, and the number of married people, I calculate the refined divorce rate, or the number of divorces per 1,000 married people. That fell another 3% for both women and men in 2018, to 15.9 and 14.3 respectively (the rates differ because these are self reports and women report more).

When I run the model from the paper again on the new data (on women only), I can show the drop in the adjusted odds of divorce, updating Figure 1 of the paper (the 2018 change in an unadjusted model is significant at p=.06; adjusted is p=.14, the adjusted change from 2016 is significant at p=.002).

For other takes on the latest data, see this report on the marriage-divorce ratio from Valerie Schweizer, and this on geographic variation from Colette Allred, both at the National Center for Family and Marriage Research.

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• The data and code for the paper are available here. This update uses the same code with one new year of data.
• If you like my new Stata figure scheme (modified from Gray Kimbrough’s Uncluttered) you’re welcome to it: here.
• Slides from my presentation this fall at the European Divorce Conference are here.
• Divorce posts are gathered under this tag.

Family diversity, new normal

Family diversity is not just a buzzword (although it is that), and it’s not just the recognition of diversity that always existed (although it is that). There really is more actually-existing diversity than there used to be.

In The Family, I use a figure with five simple household types to show family conformity increasing from 1900 to a peak in 1960 — and then increasing diversity after that. I’ve updated that now for the upcoming third edition of the book.

In 2014, I wrote a report for the Council on Contemporary Families called “Family Diversity is the New Normal for America’s Children,” which generated some news coverage and a ridiculous appearance with Tucker Carlson on Fox & Friends. A key point was to demonstrate that the declining dominant family arrangement after 1960 — the male-breadwinner-homemaker family — was replaced by a diversity of arrangements rather than a new dominant form. Here I’ve updated one the main figures from that report, which shows that “fanning out from a dominant category to a veritable peacock’s tail of work-family arrangements.”

For this update, I take advantage of the great new IPUMS mother and father pointers to identify children’s (likely) parents, including same-sex couple parents who are cohabiting as well as those who are married. Census doesn’t collect multiple parent identifiers in the Decennial Census or American Community Survey, and IPUMS has tackled the issue of how to best presume or guess about these with a consistent and well-documented standard. In this figure, 0.42% of children ages 0-14 (about 250,000) are living in the households of their same-sex couple parents. I also rejiggered the other categories a little, but the basic story is the same.

I published a version of this figure for K-12 educators in Educational Leadership magazine in 2017. I wrote:

Today, teachers need to have a more inclusive mindset that recognizes the diversity of family structures. Although there are reasons for concern about some of the changes shown in the data, the driving factors have often been positive. For example, changes in family roles reflect increased educational and occupational opportunities for women and greater gender equality within families. Fathers are expected to play an active role in parenting—and usually do—to a much greater degree than they did half a century ago.

My advice to teachers is:

The key points of diversity in family experiences that teachers should watch for are family structure (such as who the student lives with), family trajectories (the transitions and changes in family structure), and family roles (who cares and provides for the student). Using principles from universal design, teachers can promote language and concepts that work for all students. Done right, this is an opportunity to broaden the learning experience for everyone—to teach that care, intimate relationships, and family structures can include people of different ages, genders, and familial connections.

So that’s my update.

Man woman couple height, updated

I had a popular post in 2013 called, “Why taller-wife couples are so rare,” a title given it by my old Atlantic editor, who ran it under a picture of Nicole Kidman (5′ 11″) with her second shorter husband. I also put a version of it in my book Enduring Bonds, and reference it in The Family. In it I used data from the 2009 PSID to show that people are more likely to pair up as taller-man-shorter-woman than would be expected by chance. I’ve now updated it with the 2017 PSID data. This is a revised version of that post with the new data.

Men are bigger and stronger than women. That generalization, although true, doesn’t adequately describe how sex affects our modern lives. In the first place, men’s and women’s size and strength are distributions. Strong women are stronger than weak men, so sex doesn’t tell you all you need to know. Otherwise, as retired colonel Martha McSally put it with regard to the ban on women in combat positions, “Pee Wee Herman is OK to be in combat but Serena and Venus Williams are not going to meet the standard.”

Second, how we handle that average difference is a matter of social construction: We can ignore it, minimize it, or exaggerate it. In the realm of love and marriage, we so far have chosen exaggeration.

Consider height. The height difference between men and women in the U.S. is about 6 inches on average. But Michael J. Fox, at five feet, five inches, is shorter than almost half of all U.S. women today. On the other hand, at five-foot-ten, Michelle Obama is taller than half of American men. So how do people match up romantically, and why does it matter?

Because everyone knows men are taller on average, straight couples in which the man is shorter raise a problem of gender performance. That is, the man might not be seen as a real man, the woman as a real woman, if they don’t (together) display the normal pattern. To prevent this embarrassment, some couples in which the wife is taller might choose to be photographed with the man standing on a step behind the woman, or they might have their wedding celebrated with a commemorative stamp showing her practically on her knees—as the British royals did with Charles and Diana, who were both the same height.

They were the same height pic.twitter.com/I9YVAcU4hn

— Philip N Cohen (@familyunequal) March 18, 2017

But the safer bet is just to match up according to the height norm. A study from Britain measured the height of the parents of about 19,000 babies born in 2000. They found that the woman was taller in 4.1 percent of cases. Then they compared the couples in the data to the pattern found if you scrambled up those same men and women and matched them together at random. In that random set, the woman was taller in 6.5 percent of cases. That means couples are more often man-taller, woman-shorter than would be expected by chance. Is that a big difference? I can explain.

For illustration, and to compare the pattern with the U.S., I used the 2017 Panel Study of Income Dynamics, a U.S. survey that includes height reported for 4,666 married couples. These are the height distributions for those spouses, showing a median difference of 6 inches.

Clearly, if these people married (and didn’t divorce) at random we would expect the husband to be taller most of the time. And that is what we find. Here is the distribution of height differences from those same couples:

The most common arrangement is the husband six inches taller, and a small minority of couples—2.7 percent—are on the left side of the line, indicating a taller wife.

But does that mean people are seeking out taller-husband-shorter-wife pairings? To answer that, we compare the actual distribution with a randomized outcome. I made 10 copies of all the men and women in the data, scrambled them up, and paired them at random. They I superimposed the two distributions — observed and random — which allows us to see which arrangements are more or less common in the actual pairings than we would expect by chance:

Now we can see that from same-height up to man-8-inches-taller, there are more couples than we would expect by chance. And below same-height—where the wife is taller—we see fewer in the population than we would expect by chance. There also are relatively few couples at the man-much-taller end of the spectrum—at 9 inches or greater—where the difference apparently becomes awkward, a pattern also seen in the British study. In the random distribution, we would expect 10 percent of couples to have a taller wife, but we only see 7 percent in that range in the observed data.

You could look at this as people marrying to conform more closely to a norm of husbands being 6 inches taller, because there is reduction in both tails. But that left tail just happens to be pulled down right after you get to taller wives, so you could also call it a taller-man norm.

Humans could couple up differently, if they wanted to. If it were desirable to have a taller-woman-shorter-man relationship, it could be much more common. In this sample, 27 percent of women could marry a shorter man, if they all insisted on it. Instead, people seem to exaggerate the difference by seeking out taller-man-shorter-woman pairings for marriage (or maybe the odd taller-woman couples are more likely to divorce, which would produce the same result).

What difference does it make? When people—and here I’m thinking especially of children—see men and women together, they form impressions about their relative sizes (and related capacities). Because people’s current matching process reduces the number of woman-taller pairings, our thinking is skewed that much more toward assuming men are bigger.

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I put the Stata code for this analysis up here.

Thanks to Sebastian Karcher, who figured out for me that 34 percent of women could marry a shorter man.

Special thanks to Jeff Spies, who talked me down on Twitter the other night when I thought the results were all different, and then went and got the data and showed me that I was wrong, which led me to fix it, in the process restoring my faith in the resilience of patriarchy.

New working paper: The rising marriage mortality gap among Whites

I wrote a short working paper on U.S. mortality trends for the last decade. You can go straight to the paper on SocArXiv, or the code and output, if you want the full version.

The issue is that premature mortality has been rising for Whites, partly because of the opioid epidemic and also from suicide and alcohol, and also from other causes related to stress and hardship. (See, e.g., Case and Deaton, and Geronimus.) And a recent NCHS report showed that mortality nationally declined much more for married people since 2010.

So I got the Mortality Multiple Cause Files from the National Center for Health Statistics, for two years: 2007 and 2017. These are a complete set of death certificates, which include race/ethnicity, marital status, and education. I linked these to the American Community Survey, to create age-specific mortality rates by age, sex, marital status, and education, for non-Hispanic Whites, Hispanics, and Blacks, in the ages 25-74 (old enough to finished with college, but too young to die).

The basic result is that virtually all of the growth in premature death is among Whites, and further among non-married Whites. (Whites still dies less than Blacks, and more than Hispanics, at each age and marital status.)

Here is the figure of age-specific mortality rates, by race/ethnicity, sex, and marital status for 2007 and 2017. At the bottom of each column I calculated “marriage mortality ratios,” which are how much more likely single people are to die than married people. Note these death rates are deaths per 10,000, but they’re on a log scale so you can see changes where rates are very low.

In the figure you can see how much the marriage mortality ratio jumped up, for Whites only. Now, at the most extreme, single White men age 35-39 are more than 4-times more likely to die than married White men (that’s in the bottom left).

Then I zoom into Whites specifically, and do the same thing for four levels of education:

In the lowest education group of Whites (the far left), mortality rates for married and single people increased similarly, so the marriage mortality ratio didn’t increase. However, for the other education levels, death rates increased for single people more than married people, so the ratio increased (across the bottom). Even among White college graduates, there were increases in mortality for single people. I did not expect that.

My bottom line is that marriage is taking an ever-more prominent place in the social status hierarchy, and now we can add growing mortality inequality, at least among Whites, to that pattern.

Early version, comments welcome!

Why we need open science in demography, and how we can make it happen

“Why we need open science in demography, and how we can make it happen” is the title of a talk I gave at the Max Planck Institute for Demographic Research yesterday, as part of an open science workshop they hosted in Rostock, Germany. (The talk was not nearly as definitive as the title.)

The other (excellent) keynote was by Monica Alexander. I posted the slides from my talk here. There should be a video available later. The organizing committee for the event is working to raise the prominence of open science discussions at the Institute, and consider practices and policies they might adopt. We had a great meeting.

As an aside, I also got to hear an excellent tutorial by E. F. Haghish, who has written Markdoc, a “literate programming” (markdown) package for Stata, which is very cool. These are his slides.