Personality Type(s)

Just to see where, or what, I am now, I took a redacted version of the Myers-Briggs Inventory, the one available over at [HumanMetrics]( I think I scored pretty close to what I scored on the complete inventory when I took it back in the late 90s: [INFP](

* Introverted (11%)
* iNtuitive (38%)
* Feeling (12%)
* Perceiving (11%)

Maybe I was INTJ last time, but I was equally borderline. The only strong tendency here is **intuitive**, which I think was also the case a decade and a half ago.

So, apparently, sensing is out.

Find out for yourself.


[The Historian’s Macroscope](, a self-described “experiment in writing in public, one page at a time, by S. Graham, I. Milligan, & S. Weingart” is well worth a read. For now, its focus is on topic modeling and network studies. Combine that with William Turkle’s and Adam Crymble’s [The Programming Historian]( and you’ve got a reasonably good foundation for getting started.

Monday Morning Links

I am reviving a blogging tradition because some days you just have lots of tabs open in your browser and it’s all interesting. Sometimes those links get shot out as individual dishes — and there are plenty of blogs that really make that their business (no names here, but you’ve encountered it) — and sometimes you get served a buffet:

* [TYWKIWDBI][] (pronunciation is provided) notes that “A graph of 40 years of data from the U.S. Census Bureau, shows that fewer American households are comprised of married couples with children. Now there are more men and women living alone, and other “nontraditional” arrangements. … As more Americans are opting to live alone than ever before, that now seems like an entirely unremarkable choice. But for years we’ve been building houses for that big nuclear family that’s now less common. And housing data released earlier this summer by the Census Bureau, illustrated at right, suggests that the U.S. is now a country where many people live alone in a land of 3-bedroom houses.” The two graphs accompanying the post are worth viewing.
* [][] reports that the evidence for **water on mars** is pretty overwhelming. There’s a slideshow available for those, like me, who enjoy space porn. *Water! Mars! Let’s go!*
* [The Octomatics Project][] argues that a number system based on 8 or 12 makes more sense — and those interested in the dozenal system (useful when you are trying to discuss time with a fourth grader!) should know that there is a [Dozenal Society of American][] (really, not making that up, but I would if I were writing a novel about maths at war … say, maybe I will!). I especially like the graphic that advances a numerical notation that “looks more technical”:

Octal Number Notation

[The Octomatics Project]:
[Dozenal Society of American]:

Middling Data

I’ve been enjoying working through Matthew Jockers’ [Text Analysis with R for Students of Literature]( and following the various discussions about topic modeling and other approaches to “big data” in the humanities on Twitter (and elsewhere — and I really do wish there was more of the elsewhere — more on this in a moment). At the same time, I am, some would argue desperately, trying to teach myself not only the Python language, and to learn the basic terms of computer science but also trying to get a basic grasp of the statistics that lies behind so much of this work.

I do so because not only do these realms fascinate me and, I think, have real possibilities for studying the kinds of texts that I like to study but I would also like to be part of that larger conversation about what dimensions of statistics are useful, and what are not, that the digital humanities will eventually have to have as the “digital” falls away. We will at some point get past the initial, and very exciting, phase of experimentation and grabbing at all the shiny toys, and begin to synthesize these experiments into the ongoing development of the continuum of work that stretches from the humanities to the human sciences.

Folklore and anthropology have long been the kissing cousins on either side of the perceived divide between those two orders, and I am fascinated, in watching the adaptation/adoption of corpus linguistic methods, often linked with information science and various forms of artificial intelligence, with the jump from sentences, or huge gatherings of sentences into things like corpora, to novels.

These is, I think, a middle ground. It’s not the “small date” of the old humanities, nor yet the “big data” which is our current fascination, but something more like middling amounts of data. *Medium data*? (That sounds better than *middling*, but it does suggest a statistical process, no?)

*Middling data* for now, I think.

I am using it to describe the 50 some-odd legend texts I have that range in size from around 100 words to over 1000 words. This size of texts is, in itself, a kind of middle ground between short texts like proverbs and longer texts like myths. (Some oral histories I have collected tend to fall on the shorter end of this range, as well as a number of personal anecdotes, which only means that we have a lot of counting to do in folklore studies to begin to establish things like this. Easy peasy work and still terribly interesting — how many words does a given context require either to reinforce the current reality or to conjure up an alternate one?)

50 texts of 500 words doesn’t seem like too much, does it? (I’m going to go for the middle number of 500 here, just for the sake of argument.) Why that’s only 25,000 words, a long-ish short story from a literary scholar’s point of view. But 50 distinct texts begins to stretch the boundaries of working memory for most human analysts, and certainly as that number grows, one begins to require alternative means of “holding” the texts in some sort of analytical space.

Of course, as the number grows, one needs to effect some kind of compression somewhere in the process. Where and how is why we need statistical reasoning to better inform how we proceed. (Sorry for the surfeit of adverbs there.) And I do love the kinds of things that topic modeling can do, as well as other forms of statistical analyses. Certainly achieving semi-accurate results with a minimum of failures and making effective use of available computational resources is of interest to computer scientists, but I don’t, at this point, particularly care about such things. Rather, I am interested in those forms of manipulation which let me explore a collection of material(s) — perhaps formally organized enough to begin to be something like a corpus but perhaps not.

This middle ground is the ground I want to work for the foreseeable future. It will let me explore the computational and statistical possibilities from within a territory that I can still attempt to grok using old-fashioned, dare I say “analog”?, methods methods. It’s this kind of middle ground work that made Moretti’s _Graphs, Maps, and Trees_ so compelling. (And he seems to have a distinct preference for working with middling data, if I read other essays and understand other talks he has given correctly.)

*Middling* data is a terrible name to be sure, but like the “middling” domains of folklore studies and cultural anthropology, domains often viewed from a certain askance perspective by practitioners in domains more central to either side of the divide between the humanities and the human sciences, I think that there are some terribly productive tensions to be more clearly articulated and discussed.

Then again, I would think that, wouldn’t I?

The Hutt Gambit

Okay, the things people go through in order to make episodic fiction actually fit the fictional worlds it momentarily instantiates often [makes my head spin]( People! Han Solo is not a person. He is a character. George Lucas, as many people have repeatedly said, was making it up as he went along, which resulted in the whole “Han shot first” controversy. That is the fun of fictional worlds, their flexibility, and the difference from the real worlds which most of us inhabit — which have shown a rather remarkable rigidity over the years. (Much to my dismay, because I can assure you that, given the opportunity, there are quite a few people I would write out of my life and various fateful decisions I have made that I would change to have a different fate.)

Conference Announcement: “What Affordance Affords”

November 25-27, 2013 – Darmstadt, Germany

The interdisciplinary conference features contributions from the
perspectives of philosophy, sociology, psychology, science studies,
economics, architecture and design. It investigates the concept
‘affordance’, its history and transformations as it traveled through
different research fields and disciplines. The notion of affordance
originates and is frequently identified with ecological thinking, it
appears in considerations about interdependencies and interactions,
about relational configurations and ontologies. Digital objects, smart
materials, chemical devices, robots and the human body, geographical
information systems and neuronal activity, hydrological infrastructure
and landscape parks – all of these are presented and discussed as
providing or being affordances. A variety of epistemological positions
will be defended, different ontological claims advanced and relevant
background theories invoked. There will be advocates of the notion and
its heuristic value, and skeptics seeking to critique its current

Conference Website:

Simpson’s Paradox

In the middle of a great explanation of [Simpson’s paradox]( comes this:

> fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects. [=humanities]

Thanks to Scott Weingart for the heads up. (Can’t find the original tweet right now.)

Uncertainty Quantification

[Uncertainty quantification]( has a nice ring to it. Of course, human beings have been doing this kind of thing for a long time. It’s not quite clear to me what threshold we crossed, but at least a few scientists think we have and that we need a name for that threshold.

People, People, People

> The world’s most successful organizations value great people. Some call them “A Players.” Others call them “stunning colleagues.” In all cases, high talent density is everything. What’s in flux today is what makes someone great. Legacy HR models tend to value “managers” – people with graduate degrees from prestigious business schools with years of experience leading initiatives in their chosen field. As a result, a typical day in corporate America is peppered with meetings and PowerPoint presentations. Planning has become the work. Intuitively, we know that’s not right. To win in the marketplace, someone has to create and deliver exceptional products, services, and experiences, and planning won’t get us there. In a Digital OS, the emphasis on People is all about making. “Makers” are people who have skills (as opposed to credentials). They think by doing: experimenting, testing, and learning. Within these high performance cultures management has evolved into something more akin to mentorship. The thinking goes, if workers are capable of making decisions about their priorities and workflow, what’s left for the manager is skills development, knowledge sharing, and helping with roadblocks – the Montessori method gone corporate.

From “[The Operating Model That Is Eating The World](” by Aaron Dignan.