The RDF equivalent of “If you can’t say anything nice, don’t say anything at all” is “If you can’t assert something, then don’t assert anything at all.”
I’m building a language designed to be natural with linked data just as programs today are natural with local memory. The result is highly functional and data-relative in nature and reminds me of how XSLT works relative to XML nodes (e.g., a current node, child nodes, ancestor nodes). I have quite a few tests passing for the parser and core engine, so now I’m branching out into libraries and seeing what I can do with all of the pieces.
A few months ago, I accepted a job outside the academy. This doesn’t mean that I’m abandoning digital humanities. In this post, I lay out what I want to do in DH going forward. The common thread through all this is that I believe linked open data is the best way to break down the silo walls that keep digital humanities projects from sharing and build on existing data.
Continue Reading My DH Agenda
With the recent postings elsewhere about Markov Chains and text production, I figured I’d take a stab at it. I based my code on the Lovebible.pl code at the latter link. Instead of the King James Bible and Lovecraft, I joined two million words from sources found on Project Gutenberg:
- The Mystery of Edwin Drood, by Charles Dickens
- The Secret Agent, by Joseph Conrad
- Superstition in All Ages (1732), by Jean Meslier
- The Works of Edgar Allan Poe, by Edgar Allan Poe (five volumes)
- The New Pun Book, by Thomas A. Brown and Thomas Joseph Carey
- The Superstitions of Witchcraft, by Howard Williams
- The House-Boat on the Styx, by John Kendrick Bangs
- Bulfinch’s Mythology: the Age of Fable, by Thomas Bulfinch
- Dracula, by Bram Stoker
- The World English Bible (WEB)
- Frankenstein, by Mary Shelley
- The Vision of Paradise, by Dante
- The Devil’s Dictionary, by Ambrose Bierce
If we took stock of everything that we know and compared it to what we don’t know, we’d find that we know a lot about almost nothing. As we explore new things, we need tools which give us an idea of what we’re working with even when we don’t know what it is. In textual scholarship, we like to do close readings: understanding all the nuances of a text word by word so that we can tease out almost hidden meanings that rely on us understanding the text as well as its context. Sometimes, we don’t have a text or a context, but the effect of the text upon an audience. Or, to put it in more practical terms, we can’t tell what goes on inside an author’s mind, but we do have the resulting text. What can we learn about that mind from the text it produces?
Last week, I wrote about how mobs might be predictable. One of the first tools that I mentioned was autocorrelation. This is a basic tool that we will use with the others in the list, so it’s important to understand exactly what it does. That’s what I want to explore this week.
Let’s go back to high school geometry. We can define several properties and operations in terms of the angles and sides of the parallelogram to the right, though we’ll need to dive into the cartesian coordinate system a bit to see how to move on to the next step towards the autocorrelation.
We want to look at what it means to do mathematical operations on these line segments. We know that we can add numbers together to get new numbers, but what does it mean to add line segments? If we take the segment from D to E, and add the segment from E to B, it’s obvious that we end up with the segment from D to B. But what’s not as obvious is that if we take D to E and add from E to C, we end up with D to C.
As a kid, I read Asimov’s Foundation series in which Hari Seldon develops a mathematical description of society called psychohistory. The science in the books is completely fictional, but it always sat at the back of my mind. What if there was a kernel of truth in the fiction? What if people could be predictable?
Psychohistory has two main axioms (taken from the Wikipedia entry):
- that the population whose behaviour was modeled should be sufficiently large
- that the population should remain in ignorance of the results of the application of psychohistorical analyses
The first axiom has an analogy in statistical physics: the number of particles should be sufficiently large. A single atom doesn’t really have a temperature because temperature is a measure of how quickly disorder is increasing in a system. A single atom can’t increase its disorder, but it can have an energy. It just happens that the rate of entropy increase is proportional to the average energy of a group of particles, so we equate temperature with energy and assume that a single atom can have a temperature. The entropy-based definition of temperature is more general than the energy-based definition: it allows negative temperatures.
The second axiom is similar to what you might expect for a psychology experiment: knowledge of the experiment by the participants can affect the outcome. For example, using purchasing data instead of asking someone outright if they are pregnant because sometimes the contextually acceptable answer will trump the truth.
The important thing is that people are predictable in aggregate. This is what allows a political poll to predict an election outcome without having to ask everyone who will be voting, though polls aren’t perfectly predictable in part because someone will be more likely to tell a pollster what they think is socially acceptable, which might not show how they vote when they think no one is watching, thus reinforcing the need for the second axiom.
You can order a print copy from CreateSpace. Use the discount code XMXXKGKU to get 25% off.
The print cover is different from the digital, but I still tried to put together a cover that was somewhat connected to the novel. The digital cover reflects the role of fours and a virtual world tree. In the case of the print edition, the artifacts resemble meshing gears, cycles enmeshed with cycles, and discarded materials half buried in the sand, similar to the layers of conspiracy in the story feeding off of each other and only half emerging from the text.
The next step is to match up the print and digital editions on Amazon so that you can get a copy of the digital edition when you buy a copy of the print through Amazon’s Kindle Matchbook program.
I’m slow writing novels. I’ve drafted the first half (70,000 words) of a new one with the working title Silent Rain (you can see how slow I’ve been if you’ve noticed the yellow progress bar in the sidebar that hasn’t moved in almost a year). Now I’m going back and editing it down to refresh my memory of the story in preparation for starting a push through the second half in November for NaNoWriMo. I don’t expect to have the editing finished over the next month and a half, but I do plan on releasing the first half as a standalone work in early spring while I wrap up the second half.
Meanwhile, I thought I’d share the beginning of the novel with you so you can see where it’s going, or at least how it starts. This is after a first edit to get rid of much of the slow sections and tighten the dialogue. Other rounds will deal with other aspects of the text.
I stood in line for a few hours the day the iPhone came out in 2007. I had been using various cell phones before then, but the iPhone was revolutionary. I didn’t have to wade through sales pitches and confusing marketing to figure out which features I needed to pay for. Everything was included for a single price, and the price only depended on how many minutes I needed each month.
Cell phone companies have recovered some ground. Monthly fees depend on how many minutes AND how much data you want, as well as whether or not you want to tether a laptop or other device to the phone (that was always off the table with the first iPhone). If you want to upgrade more often than every two years, that’s another new monthly fee. Not quite as bad as before the iPhone, but getting more complicated so you don’t realize just how much you’re paying for spotty service. Until we have real competition in the cell market, this will be our future.