A Short Note on Long Article Titles: And the Use of Colons

A few years ago, OrgTheory had a brilliant post tracking the trend of increasingly long article titles, and the growth in the use of colons and subtitles, in the journal Administrative Science Quarterly. The main finding was that average article title length had grown from 9 to 15 words from 1958 to 2008, and colon usage had gone from 20% to something like 90%.

A couple years before that, James Moody published a short piece in the American Sociologist, Trends in Sociology Titles. Moody’s findings were similar: mean words grew from 7 in 1935 to 12 in the early 2000s. Colon usage went from near 0 to 75% in the late 90s. Moody also rejects a suggestion proposed by Howard Becker a few years before that in the hilarious titled “Long-Term Changes in the Character of the Sociological Discipline: A Short Note on the Length of Titles of Articles Submitted to the “American Sociological Review” during the Year 2002.” Becker argued that the trend of longer titles might be an attempt to garner more citations, with the idea being that longer titles attracted more reader attention. Moody finds the relationship between article title length and citations to be negative (but not significant). Of course, Moody’s finding doesn’t mean that authors aren’t trying to garner attention with more specific wording, it just suggests that these attempts aren’t working. It may also be endogenous – more prestigious authors can afford to be vaguer and will still get cited more often. And so on.

Edit: A quick analysis of the last four issues of ASR shows the colon trend has not stopped. Of the 26 articles in the four issues, 17 have a colon, and another 4 have a question mark or exclamation point. So, a total of 21/26 have a subtitle of some sort, about 81%.

The Internet + Foucault = Win!

A few days ago, fellow sociologist Kelan Steel Lowney and I made a little tumblr: Hey, Michel Foucault. Two nights ago, Fabio graciously linked it over at OrgTheory, and contributed a couple entries. Yesterday, it got about 6,000 hits, mostly from Facebook and Metafilter. So, I guess it turns out the internet likes a good Foucault joke.

Let us know what you think, and please suggest your own through the Tumblr submission system or by leaving a comment on this post!

Rojas, “Grad School Rulz”

OrgTheory’s Fabio Rojas* has just re-released his award-winning** collection of essays on graduate school, Grad Skool Rulz: Everything You Need to Know about Academia from Admissions to Tenure. The title is a play on words: Fabio tells you about the (previously) unwritten rules of the game in grad school, but also, grad school is pretty awesome. (Grad school here means research-oriented doctoral degrees, not professional programs.) But, as awesome as grad school is, Fabio’s book is worth the price of admission*** for the first chapter alone:

Chapter 1: Do Not Go to Graduate School

Graduate school is not to be taken lightly. Don’t start graduate school because you don’t know what else to do with your life or you have a tough time with a regular job. If you are thinking about graduate school because you can’t find a job or hold a job, stop. Learn how to find a job and hold a job. Become someone who can learn skills and who other people want to be around. There is no point in being a journeyman academic unless you are highly self-motivated. Outside of academia, you will make more money, you’ll have the same job stability, and you will have more time.

The rest of the chapter explains briefly and bluntly what you need to get into graduate school, and some ways to tell if grad school is right or wrong for you. If an undergrad asks me to tell them about graduate school, my first answer will be to point them to Fabio’s excellent book.

* Also known as Indiana University Associate Professor of Sociology Fabio Rojas, but I think we know which title is more impressive here.
** If it wasn’t before, then I’m giving it an award right now: Best book about graduate school that costs less than a mocha at Star Bucks. Or almost anything at Starbucks.
*** 2$ Seriously, it’s super cheap!

ANT in the Wild: Time’s “Person of the Year”

Time Magazine is not exactly an outlet for high-fallutin’ science studies theorizing. Yet, a quick glance through the Person of the Year list suggests that Time Magazine and Bruno Latour might have a lot in common. Perhaps the most contentious argument made by Actor-Network Theory* is that non-humans have agency, and that the relevant units of analysis are “actor-networks” that enroll individuals, organizations**, scientific theories, machinery, and more, all on potentially equal bases. So, a mosquito, a scallop, a mass spectrometer, and a federal agency may all be actors in a particular network.

What about Time Magazine? The first person of the year (then called “Man of the Year”) award was given out to Charles Lindburgh in 1927. In 1936, Wallis Simpson was the first woman to win the honor. FDR won it three times.*** In 1950, the award was first given to a collective actor: “The American Fighting-Man”, a feat repeated a few times after (e.g. 1960’s “American Scientists”, 1966 “Baby Boomers”). In 1982, just as Latour and Callon were pushing the first influential Actor-Network Theory articles (e.g. “Unscrewing the Big Leviathan”), Time Magazine gave its person of the year award to… the Computer!

Thus far, the only other non-human or human collective to receive the honor was “The Endangered Earth” in 1988, an influential actor mobilized in a great many important networks. Still, I think Time’s willingness to include these non-humans, along with more contingent amalgamations like “Baby Boomers”, and “You”, suggests a kind of openness to the idea of what constitutes an important actor (“person”****) that is lacking in most social theory. So, kudos to Time!

* Which, to be a bit more precise, is more of an “infra-language” or ontology than a traditional theory. It’s a way of talking about what is in the world, not as much a set of causal claims or anything like that.
** As Brayden King grudgingly notes in a ScatterPlot comment about organizations’ ability to speak: “Using actor network theory as a guide (and I never thought I’d say that), an organization’s position as actor is uncontroversially not that different from saying an individual is an actor.”
** A trivia question, “Who won Time’s “Person of the Year” the most times?” inspired this trip into the history of the award. Getting it correct also helped our team win Pub Trivia and $50 last night. See Mom and Dad, sometimes being an academic pays!
****Although Time did rename the award those years to “Machine of the Year” and “Planet of the Year”. Wikipedia still, appropriately I think, places the awards in the same category, since the Computer and the Endangered Earth received awards instead of a particular person or group.

Best of the IgNobel Awards

The 2010 IgNobels are out! Here are a few of my favorites – thought admittedly, being the best of the IgNobels is a bit like the best of the 50 cent prizes.

PHYSICS PRIZE: Lianne Parkin, Sheila Williams, and Patricia Priest of the University of Otago, New Zealand, for demonstrating that, on icy footpaths in wintertime, people slip and fall less often if they wear socks on the outside of their shoes.
REFERENCE: “Preventing Winter Falls: A Randomised Controlled Trial of a Novel Intervention,” Lianne Parkin, Sheila Williams, and Patricia Priest, New Zealand Medical Journal. vol. 122, no, 1298, July 3, 2009, pp. 31-8.

PEACE PRIZE: Richard Stephens, John Atkins, and Andrew Kingston of Keele University, UK, for confirming the widely held belief that swearing relieves pain.
REFERENCE: “Swearing as a Response to Pain,” Richard Stephens, John Atkins, and Andrew Kingston, Neuroreport, vol. 20 , no. 12, 2009, pp. 1056-60.

MANAGEMENT PRIZE: Alessandro Pluchino, Andrea Rapisarda, and Cesare Garofalo of the University of Catania, Italy, for demonstrating mathematically that organizations would become more efficient if they promoted people at random.
REFERENCE: “The Peter Principle Revisited: A Computational Study,” Alessandro Pluchino, Andrea Rapisarda, and Cesare Garofalo, Physica A, vol. 389, no. 3, February 2010, pp. 467-72.

Adventures in Fact-Checking: Einstein Quote Edition

I spent Friday morning working on my first ever syllabus, for the discussion sections of an upper-level undergraduate research methods course. I was thinking of including a somewhat cheesy quote but I couldn’t remember who had said it: “If we knew what we were doing, we wouldn’t call it research.” A quick Google search revealed that the quote was almost always attributed to Albert Einstein. “Awesome!”, I thought. Always great to have an Einstein quote. But, a nagging voice in the back of my head pointed out that not one of those attributions was sourced.

Twenty minutes later, I’d exhausted my usual sources (Google scholar, Google books, etc.) and still no authoritative citation. An online consult with a librarian and some reference works on quotations revealed nothing, but did point me to the Einstein Archives Online. I dashed off an email and was delighted to get a response a few hours later: they couldn’t find anything in the usual places (the usual places mostly consisting of The Quotable Eisntein). They couldn’t assure me that Einstein hadn’t said it, but they had no obvious evidence that he had.

So, what to make of all this? Did Einstein say the attributed quote, but perhaps never in a formal or written setting? If not, who did? When did it first become associated with his name? I don’t know, and I’m out of time and energy to investigate. But I am pleased that I could draw on the expertise of a reference librarian, an archivist, and the entirety of the Google books and scholar databases from my desk in a handful of minutes. It’s a fun time to be a researcher.

Nerds vs. Geeks vs. Dorks: Round n of N

Friday night, while celebrating with some of my colleagues who had just finished their prelim exams (Congrats, all!), I engaged once more in a classic debate: geek vs. nerd vs. dork. What word means what? What activities count in which category? And so on. I remember having this debate for the first time in AP Physics in high school, and it pops up on the internets with some frequency (see, for example, 1, or 2). Most of these debates are well-reasoned, and impassioned, invoking the etymology (e.g. nerd was a word coined by Dr. Seuss, geek referred to circus performers, dork to male anatomy) and such, but few reference any data.

Any casual student of Wittgenstein* knows, however, that the meaning of a word is its use in the language. It doesn’t matter where it came from a century ago, it matters what it does – what we do and associate with it. So, let’s bring in some data! In this post, I use google hits as a good proxy for typical usages of the words nerd, geek, and dork and associated phrases. I will test some hypotheses about various activities and how likely they are to be referred to as nerdy, geeky or dorky, and similarly, I will make some arguments about what activities are most associated the terms on their own.

Hypotheses

H1 – The Dork Stands Alone: “Dork” will least often be associated with any other word, and most often stand on its own. That is, references will be to a “dork” not a “computer dork”, “gamer dork”, etc.

H2** – Nerds are Geeks, Geeks are Nerds: Most concepts will have a lot of hits for both nerd and geek if they have a lot for either. The two have become conceptually indistinct.

H3 – Phonemes Matter! : Sounds that clash will be disfavored (“Star Trek Dork” has clashing K sounds). Alliterations will be favored. And so on. If this hypothesis is supported, it could explain why these terms have not settled out culturally into three distinct categories. The three categories being somewhat overlapping allows for deviations from patterns which are phonetically favored, which in turn maintains the fuzziness of the distinctions. A larger test of this hypothesis would have to look at whole sentences, and also require someone who knows something about linguistics (which I do not).

Data and Methods

The data for this post come from Google searches performed on 8/28-29, 2010. Each of the three main terms was searched for a total hit count as a baseline. Then I searched each phrase – e.g. “Computer nerd”, “Computer dork”, “Computer geek” and recorded the number of hits. The main dependent variable for activities is the proportion of hits which are in each of the three categories. I also created a second dependent variable “normalizing” each bigram to control for the fact that dork, geek and nerd are used with different frequencies overall. Theoretically, this second d.v. is less clear in its interpretation but it seemed like a good idea. Maybe if we imagine that people are randomly choosing which of the three words – nerd, dork or geek – to use to describe a person-activity pair, we can model that decision as multinomial with baseline probabilities pNerd, pDork, pGeek, which those things being estimated by the relative frequency of each term (in this study, .345 for Dork, .222 for Nerd, and .433 for Geek). Thus the second d.v. is testing for deviations from randomness, accounting for that behavioral model, while the first d.v. is testing overall, how much is this particular activity thought of as dorky, nerdy or geeky (ignoring the fact that overall one of those thoughts is more common). Whatever floats your boat. Full data are available on request. On second thought, full data are available in this convenient google docs spreadsheet. I’m not bothering to discuss the second version of the d.v., because few of the results change much, especially in relative terms.

Findings

First, I find that H1 is strongly supported. For every search term except 1, dork underperforms its overall usage (check the spreadsheet if you want to verify, or the graphic below). That is, #hit for Dork is approximately 35% of all hits for dork, nerd and geek. But for most terms, “____ dork” appears less than 10%, and for only 1 searched term does it have more than 20% of the results share. The one glaring exception is “Star Wars”. For some reason, “Star Wars Dork” appears to be 35% of Star Wars Dork/Nerd/Geek hits.

Second, in re: the Star Wars Conundrum. Star Wars is 35% Dork, 54% Nerd, and 10% Geek. Star Trek is 1% Dork, 50% Nerd, and 49% Geek. Why is there such a huge disparity in the dork figures? Why are there so few Star Wars geeks? I read the lack of Star Trek Dorks as partial support for H3 – the two “K” sounds in the phrase Star Trek Dork make it even more unpleasant to say than it is to smell.*** The emergence and meaning of the “Star Wars Dork” are an interesting anomaly however, which calls for further research. For example, I might ask some older nerds at pub trivia tomorrow.

Third, math is geeky, but physics is nerdy. I think this attacks some of the gender-based explanations. As far as I know, math and physics are both coded as pretty masculine still (any gender and science experts out there want to weigh in?). Yet the disparity is sharp – the “Geek % – Nerd %” for Math is 33%, for Physics it’s -18%. This rules out some alternative explanations, but does not directly speak to my hypotheses.

Fourth, biology is geeky, bio is nerdy. So, along with various scientific and leisure activities, I also ran some abbreviations as a robustness check. So I ran “anthro” along with “anthropology”, and “bio” along with “biology”. The Geek-Nerd spread for Biology is 22%, for Bio it’s -65%! “Bio Nerd” is clearly a set phrase, and yet Biology as an activity is somewhat more geeky than nerdy. I interpret this linguistic overlap as strong evidence for hypothesis 2 – geek and nerd are more overlapping than distinct. Here’s a table with the relative disparity across various terms for how geeky vs. nerdy they are. See if you can find a pattern!

Last, let me present an incredible graphic portrayal of what you might call Dork-Nerd-Geek Space. Size of the circle represents the log of the total number of hits for a given term (i.e. the sum of hits from “bio nerd” + “bio geek” + “bio dork”). Location of the circle is determined by the relative frequency of dork, nerd or geek.

I thank information designer Evan Hensleigh profusely for making this absurdly awesome graphic. Check out his site and other work. As you can see in the chart, almost all of the results are along the “Nerd-Geek” axis, and have little weight on the dork side (except Star Wars). Also, as you can see on the chart, many terms are in the middle. (Note that the “used independently” circle is just that, “geek”, “nerd” and “dork” searched on their own. It represents the overall proportion of google hits.) These activities are not inherently nerdy or geeky, they are just in between. Note that the colors correspond to some rough categories – science, other academic, games, general, fiction, and music.

Conclusions

While I doubt any single work could hope to resolve the epic nerd vs. geek vs. dork debate, I hope that this post has offered some modest evidence. First, I have shown that the dork truly stands alone. Except for Star Wars. Second, I have shown that most nerdy activities are also geeky and vice versa, and when one concept dominates there is no obvious pattern behind it – e.g. not all sciences are nerdy or geeky, and so on. Third, I have offered a small bit of evidence for the hypothesis that phonetics matter, that some phrases just sound better than others. This hypothesis needs more research to be fully substantiated. If correct, however, this phonetic favoring could explain the pathway by which the fuzziness between geek and nerd was produced and is maintained. “Freaks and Geeks” is a great title, but “Freaks and Nerds” has nothing going for it, yet the two phrases have no large difference in meaning.****

I want to offer one large caveat. These results cannot fully substantiate the hypothesis that geek and nerd have become indistinct. I have shown that very few areas of nerdery are not also areas of geekery, and vice versa, but it remains possible that “computer nerds” and “computer geeks” are somehow different (for example). This claim would require deeper textual analysis or ethnographic fieldwork to check. I am relatively confident that such a detailed analysis would reveal few if any substantial and enduring differences in the categories, but cannot be certain from the data presented here. Indeed, the perennial debate over the boundaries of the categories nerd and geek itself suggests the fuzziness which I have sought to establish here.

* Doesn’t that phrase just ooze pretension? Gotta love it!
** Hint – I looked at some of the data before coming up with this hypothesis. Much like every other quant project.
*** I was raised on a healthy diet of both Star Wars and Star Trek (TOS, TNG, DS9 and Voyager), so I have no horse to back in this particular race. Can’t we all just get along? Also, I thank Evan for this interpretation of the lack of Star Trek Dorks.
**** I thank Max for this point.

#CountryDurkheim

This is why the internet is made of win. Someone started posting as Emile Durkheim on twitter, which got very popular and funny at ASA. Now, apparently, there is some sort of competition to come up with fake country song names based on Durkheimian phrases, e.g. “A boy named Suicide”, “I Got Friends in Low States of Moral Regulation”, and so on. You can watch it all breakdown in real time here. I may have to breakdown and start using Twitter “for reals” if this kind of humor keeps up.

Goffman QotD: Sex vs. Gender

I came across a piece by Goffman from an early issue of Theory and Society (1977, “The Arrangement Between the Sexes”) this morning and started reading it. Around page 4 I hit a quote I thought I’d share, because I very much enjoyed it. The quote follows a few introductory comments on gender and sexuality, and the way that gender and sexuality are laid on top of, but are very much separate from biology. Then Goffman notes:

In any case, it should be perfectly clear that gender and sexuality are not the same thing; by my understanding, at least, a seven-year-old boy who manfully volunteers to help his grandmother with her heavy packages is not trying to make out with her.

Have a good Goffman morning!

The Dragon Ball Z Model of Power

“His power level is over 9000!”

What is power?* Sociologists and other social scientists have spilled a lot of ink on the subject. On some level, the question of power is tightly connected to the basic insight of sociology, phrased variously as “we are not alone”, “our actions are not determined solely by internal features”, “society exists”, “structures constrain agency”, etc. Some recent theoretical moves have emphasized power acting on smaller and smaller scales, such as the move to conceptualize “everyday” forms of resistance – e.g. Scott’s work on Malaysian peasants, or Ewick and Silbey’s work on narratives as resistance to the law. Another tradition, in dialog with the previous and drawing strength from folks like Gramsci, looks for power in the diffuse ways that society has a hold over us – hegemony, ideology, discourse, relations of power, etc.

My point here is not to exhaustively catalog different traditions or definitions of power.** I want to instead characterize the most basic definition of power, the one we often fall back on in a pinch, and the one that comes from the granddaddy of Sociological definition, Max Weber himself. Weber defined power, in Economy and Society as: “the probability that one actor in a social relationship will get be in a position to carry out his own will despite resistance.” (53) In other words, Triangle Man has more power than Particle Man because when they get in a fight, odds are, Triangle Man wins.*** Stephen Lukes, in a fantastic essay refers to this as type 1 power, contrasting it with more subtle forms of power like choosing the ground on which battles are fought, or being able to alter the interests of the other party.

Rather than simply referring to this model of power by the dry label “type 1”, I propose we call it something sillier: the Dragon Ball Z model of power. In this model, power is something possessed (an actor has power), quantifiable (a “probability”), and fairly generic. So, in the above clip, we can see that Goku has a power level over 9000, a clearly impressive amount of power. I think by naming this model, and associating it with some hilarious images, we can begin to more easily catalog its assumptions and weaknesses. For example, the context of DBZ power is very straightforward – two individuals battling physically. But what happens when outcomes, parties, and means are more uncertain? In many interactions, multiple parties with multiple relationships and multiple, often conflicting, goals are interacting. What does power mean in that context? Etc. More basically, what happens when we relax the assumption that interests are straightforward, unchanging, and fixed in advance?

But for the moment, I just want us to acknowledge what we mean when we talk about power. And most of the time, we mean simply that someone is better able to get what they want than someone else – in other words, that their power level is over 9000!

* The snarky high-school physics student in me says, “Work over time, duh!” You should have seen me laughing reading about how Bourdieu defines fields and thinking about abstract algebra (hint: Bourdieu does not need an additive identity…).
** Lukes, who I will discuss below, does the best job of this so far, that I know of. Go read the first chapter! Though, and this is not clear, I would argue that Lukes’ scheme still can’t encompass Foucauldian diffuse power as acting on other people’s actions, etc.
*** I am being a little unfair to Weber. A more subtle reading of this definition would allow for more of the nuances that Lukes discusses. But I think the way we usually use Weber’s definition is the way I am talking about – the judge is more powerful than the defendant, because the judge can make sure he gets what he wants most of the time, etc.