Peter Organisciak

Here are two command templates that I cooked up for Ubiquity. They allow you to easily create simple call and insert commands.

The point is that if you want a collection of values for reference, it should be easy to make it happen by putting it into a Google Spreadsheet or Listable.org and pointing Ubiquity to it.

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Jeff Biggar just sent me a link to a “Petition in Support of the SSHRC.” by NDP member Niki Ashton. I was surprised to find out the following:

For more than thirty years, the Social Sciences and Humanities Research Council (SSHRC) has been promoting and supporting university-based research and training in the humanities and social sciences. SSHRC funding has been used to complete ground breaking research in countless areas in Canada and around the world.

The Federal Budget presented on January 27th contains a sentence that has the potential to halt this kind of research: “Scholarships granted by the Social Sciences and Humanities Research Council will be focused on business-related degrees”.

These measures are backward and insulting to the thousands of Canadians that are students and researchers in the social sciences and humanities.

What?! Has anybody else heard about this? While, yes, the petition’s wording of “insulting” can apply here, it’s more thansomething personal. I’m not upset because I’m in the humanities, but because this goes directly against the beliefs and values that have brought me here. After the debacle of a free market, greed-driven culture that has collectively dug our societies a hole, you would think that we would be moving towards a softer, more humanistic approach to the society of tomorrow. SSHRC scholarships reward the brightest (hi Kathleen!), and this line in the 2009 Federal budget shows that, rather than re-evaluating the vehicle, the Canadian government is looking for smart people to get extra mileage from the broken-down jalopy. It’s the capitalist take on the old communism vs. stalinism defense: “It’s not that a democracy run on cutthroat greed doesn’t work, it’s that it hasn’t be done right yet”.

In In Praise of Pattern, Stephen Ramsay makes much the same point that I made last week, that one of the most effective ways that computers can benefit qualitative, non-binary research is by breaking down texts (in the broader definition of the word) and presenting them in a way that a human can understand them in a way not possible before.

When looking for inspiration for visualisations, Ramsay went fishing, finding that nature naturally forms into its own graphs in many places, if care enough to pay attention. In this spirit of breaking down visual communication, I’d like to go through a thought experiement on visualising a basic piece of information, represented as a node. This is based on some old sketches that I found in my journal, and the node idea is influenced by the Mandala browser.

Okay, so say we have our information organized in nodes. Really, it could just as easily be a screenshot or box of information, but for now, I’ll start with a simple point. Like on a graph, it’s free in it’s own space; that is to say, the node exists in a larger, two- (or more) dimensional plane, unlike the one dimension that textual information usually follows.

Now, if have multiple nodes that need to be differentiated, we can easily show this visually through shape or fill (color, shade, texture). Say you’re plotting nodes of hit pop songs from the past fifty years. You could quickly identifier their most important characteristics by the look of the node. The gender of the lead singer could be visualized with shape (female=circle, male=square, and none=triangle). Each decade could be assigned a different color, so songs from the sixties could be blue, or songs from the eighties could be pink. Length of songs could be quickly shown by the size of the the node (i.e. longer songs could have larger nodes).

Once you have a number of nodes, you can show relationships between them. Since they exist in a multidimensional plane, distance is probably the most aparent way of showing relationship between items. Other ways include branching, which shows a flow, and orbit, which can use distance (from center) to show relevance to the main node, but also show relationships between the satellite nodes (by virtue of how close they exist in orbit).

The last form of visualising that I considered is longitudinal analysis, or showing relationships and changes over time. Traditionally, nodes are graphed with time as the independent variable. However, in computing, animation is also a useful and effective way to show temporal change. I’ve found an increasing number of visual communication relies on animation not for novelty, but to emphasize a point. This 2008 Democratic primary breakdown is a prime example (for example, click between “Whites” and “Blacks” and note how the animation brings home the point). Animation can even be combined with graphing, if there’s a different dependant-independant relationship that you hope to show. Google’s motion charts are an example of this.

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These concepts just scratch the surface. If you have any of your own ideas, feel free to share them below.

Yesterday, I attended PD Day 2009 from the School of Library and Information Studies (SLIS). There Andrew Keenan, a HuCo/SLIS student, presented on “Evaluating Sociability Online”, in which he tried to identify the distinguishing features of two popular social networks — Facebook and Myspace — and two niche social networks — Twitter and LinkedIn. In his conclusion, he found that each represented a different paradigm in its success: Facebook as private (closed connections based on real-world identities), Myspace as public (persona building and extravagant), Twitter as technology (doing one thing extremely well), and LinkedIn as community (gathering around a commonality, akin to message board communities). The separation helps explain how there’s roomm for each of these, though Keenan argues that community-based websites will decline in preference of Facebook, while technology-based one-use websites will explode in popular. I agree with the latter sentiment, especially since such “do one thing well” sites are perfectly fit for pluging into Facebook Platform or OpenSocial.

Keenan’s presentation was extremely refreshing, and I’ve identified why: he understood what he was talking about. I read about Facebook much more than I use it, and it seems that, when on the topic of the service, the tin-foil hats come out and commentators lose all sense of reason. Often, these are commentators from established media that simply don’t consider the big picture view of social networking in human communication. Having spent many months teaching journalists,  I can attest to having witnessing this firsthand. Having, I have seen such losss of reason as apparently in school, with young people similarly having a reaction to such recent communications upheaval.

It should be noted that there were people in the audience with their apparently-rehearsed speeches, shocked and ready to attack Keenan on his evaluation of Facebook. “Facebook isn’t private!” was met with many ‘yeas’. “Even if you have a fake name and delete your account, they’ll still have you photos” was another odd comment. Even the keynote speaker pitched it, mentioning Facebook is a business that can do whatever it wants with your data and that it violates Canadian privacy laws because the data that you given it is hosted on servers that are possibly in America. Suddenly, the fresh air that was Keenan’s presentation dissipated into a hot air. What more, within the context of the presentation, the point was right on:  in privacy studies on social networks, Facebook has come out on top with the flexibility and strength of its privacy features. As Keenan noted in a response: privacy problems on Facebook are generally a user-issue, not a systems-issue. This is well addressed by James Grimmelmann in Facebook and the Soocial Dynamics of Privacy:

The first task of technology law is always to understand how people actually use the technology. Consider the phenomenon called “ghost riding the whip.” The Facebook page of the Ghost Riding the Whip Association links to a video of two young men who jump out of a moving car and dance around on it as it rolls on, now driverless. If this sounds horribly dangerous, that’s because it is. At least two people have been killed ghost-riding1, and the best-known of the hundreds of ghost-riding videos posted online shows a ghost rider being run over by his own car.

Policymakers could respond to such obviously risky behavior in two ways. One way—the wrong way—would treat ghost riders as passive victims. Surely, sane people would never voluntarily dance around on the hood of a moving car. There must be something wrong with the car that induces them to ghost ride on it. Maybe cars should come with a “NEVER EXIT A MOVING CAR” sticker on the driver-side window. If drivers ignore the stickers, maybe any car with doors and windows that open should be declared unreasonably dangerous. And so on. The problem with this entire way of thinking is that it sees only the car, and not the driver who lets go of the wheel. Cars don’t ghost ride the whip; people ghost ride the whip.

…

Over a hundred million people have uploaded personally sensitive information to Facebook, and many of them have been badly burnt as a result. Jobs have been lost, reputations smeared, embarrassing secrets broadcast to the world.

It’s temptingly easy to pin the blame for these problems entirely on Facebook. Easy—but wrong. Facebook isn’t a privacy carjacker, forcing its victims into compromising situations. It’s a carmaker, offering its users a flexible, valuable, socially compelling tool. Its users are the ones ghost riding the privacy whip, dancing around on the roof as they expose their personal information to the world.

Keenan’s presentation slides are available at the PD Day 2009 website. What do you think? I haven’t yet addressed any of the nay-sayers’ issues, but if you’d like to hear some on that, feel free to start a debate.

The absolute most important part of collective action is the collective. At the same time, it the the most difficult and unpredictable piece of such an effort. For many otherwise good ideas, the lack of a crowd deals a critical blow to their success.

In collecting a crowd, one should consider the incentives that motivate that crowd. However, collecting a crowd in not the only way to gain one. What I’m talking about is repurposing a crowd.

While obviously not an option for everybody, repurposing a crowd offers, in many cases, the best chances for crowdsourced success. This may entail piggybacking functions onto your widely-used product (especially useful if you’re Google or Yahoo—which most of us aren’t) but it can also mean borrowing from somebody else’s (like with Facebook platform). The point comes down to this: it’s hard to provide incentive for users to frequent a new site in their online routine, but it is much easier to utilize the sites that they’re already visiting for other purposes.

Here are three directions to consider.

1. Making an audience into participants

Sometimes you have to make do with what you have. When you’re a local radio station competing with a television network, “what you have” can seem frustratingly limited. When it comes to traffic reporting, the difference may be that the big guys have helicopters in the sky and cameras on busy road. How can you compete with that?

For radio stations, the answer lies in what they do have: an audience actively experiencing the traffic. What has emerged from is traffic reporting based on mobile phone audience tips. This crowdsourced model helps narrow the competitive gap that expensive technologies create.

The magic lies in the clever mobilization of readers. There is already a large, dormant audience armed with the information that a traffic watch needs. Giving that audience a voice is all that is needed to get the information. A similar example is Are You Being Gauged from WNYC.

2. Crowdsource as a feature, not as the main event

Some of the most useful examples of crowdsourcing in the wild formed on the backs of other products. The golden standard here is the tagging feature of Flickr. Users have no rules forced upon them on how to encde their information. They just want to put up their photos, and tagging is something that is simply offered for them to stay organized. However, on the larger scale, all the users that do end up using tags helps create an extremely semantically relevant corpus of images (and, as I’ve mentioned before, the first place you should go when looking for images).

Returning to the earlier example of traffic information, such “incidental crowdsourcing” is being tested in using cellphone tower information to determine how fast traffic is moving. Simply by having one’s cellphone in their cupholder, they’re contributing data. Similar to this are e-commerce recommendation engines, where simply by surfing a site, a user contributes to an algorithm for predicting what similar users would want.

3. Reimagine Popular Actions

In this form of repurposing crowds, the question come down to how one can squeeze extra juice out of something already being used. his is the approach that Luis von Ahn projects take, especially well epitomized in reCaptcha and the ESP Game. If people are already using captchas, why not have them also digitize scanned texts? If people like to relax with an online game, why not also have them encode image metadata?

Before starting a crowd-assisted project, don’t bet on people finding their way to it. Think about how existing groups and communities can be used and you’re much more likely to succeed.

Humanities Computing seems to be an oxymoron, the two words at war from opposite ends of the spectrum. After all, to compute is to calculate, but that with which the humanities concerns is inherently abstract. In humanities, you  don’t mathematical calculate; you analyze, you interpret and you understand. It’s the very exploration of the ‘humanness’ of humans.

Given that, it’s a bit trickier seeing how computers can benefit humanities research. They certainly can’t comprehend the splendor of a Shakespearean text, and critique a piece of Renaissance art. However, for what they cannot do themselves, there are a number of ways that they can assist us in doing it. They can scale projects from the few to the many, like with Wikipedia. They can assist our workflows, with everything from data processors to annotation tools. And, they can process data and spit it out in new ways, offering us new opportunities to analyze it.

The latter is what text analysis does. A computer can digest the entire works of Jane Austen and find patterns that would be too difficult for most humans. However, in seeing these patterns, a human can realize something new about the way that Jane Austen wrote, gaining a better understanding of her work.

Last year, I did a study researching American media coverage of strikes in France. At some point, I decided to run the articles that I was analyzing through TAPoR’s text analysis tools. When you run “List Words”, you get sparklines (which are little, inline, bar graphs) of the top couple of words. I soon began to notice that words like “president” and “government” consistently were at the beginning of articles, while words like “union” began to rise near the end. This very strong trend allowed me to form a hypothesis that guided me in my work.

Note that amongst the capabilities of computers, such analysis, based on presenting a text in new ways, is the first step in technological capabilities. When computers were relatively primitive, math based analysis was the extend of their abilities. This, I’ve come to believe over the past few months, is why Humanities Computing has developed such a strong tradition of text analysis: it’s simply where the field is rooted in. It’s tradition. Today, with much more possibilities for computing to benefit the humanities, the field is not as strongly defined. The term ‘Digital Humanities’ is picking up steam, because HuCo is no longer simply about calculations, and can no longer be described by a single verb.

Update: Comments are go!

I just realized that I had taken out comments in my design of this site, never intending to “blog” with it. Well, indeed, I’ve been doing just that, so I’ll add them back into the theme for all categories under the umbrella of “Blog”.

If you had anything deep to say, check back tomorrow.

Justin Ouellette, creator of the wonderful (though eventually shut-down) mixtape-sharing site Muxtape, has created ihardlyknowher.com, a wonderful browser for Flickr images. Really, this is the ONLY way to view Flickr images.

The reason that I bring it up is that it’s so simple: something which most of us could have implemented. A good idea goes a long way.

Also, for the first time, I feel that I can share my Flickr without some of the experience being lost. Go to http://ihardlyknowher.com/organisciak and scroll to the very bottom to change the size from “smaller” to “larger”. Share your own Flickr sets in the comments.

With the text as an “ordered hierarchy of content object”  theory still fresh in my mind, I wanted to share something perhaps strange: my favourite object notation model. The reason that I want to share is to show how powerful and encompassing such a simple structure can be. What I want to talk about is JSON.

JSON (JavaScript Object Notation) is simply a collection of arrays and objects. I’ll describe what those are momentarily, but what you need to know is that they are simply incrementally more complex values. A value is simply one item. For example:

hamburger

That’s it. If you want to set it to a variable (a saved, named value; think of the use of letters in algebra as placeholders for numbers or formulae), it would something like the following:

myLunch="hamburger";

Note that I added quotation marks, to show that the value is a string. This way, the computer will not confuse it for another variable.The semi-colon also shows that it is the end of the  formula: I’ll get  to the significance of this later.

Moving on,  an array is a list of values rather than simply one. In JavaScript, such lists exist within square brackets, as such:

myLunch = ["buns", "patty", "tomato"', "lettuce"];

To reach a specific item in an array, one simply has to ask for the index location. Each value is numbered, starting with zero, so to ask JavaScript for myLunch[2], JavaScript will tell give you “tomato”.

While an array is a simple list, separated by commas, an object is a list where each value is labelled. These are called “name-value pairs”, because each value is combined with a name. In JavaScript, and therefore JSON, the two are separated with a colon. So, this would be an object:

myLunch = {"ingredient1":"buns", "ingredient2":"patty", "ingredient3":"tomato", "ingredient4":"lettuce"};

Here, instead of requiring an index place for finding a value, you simply need to know its name. So instead of myLunch[2], using myLunch.ingredient3 would return “tomato”. Note also that curly braces are used for objects, while arrays used square brackets.

Now, JSON is not only simply, it’s intended to be easy to read. As a result, line breaks and tab stops make no difference. This is why the semi-colon is important: simply pressing Enter won’t end your formula. So, the above object can be written as:

myLunch = {

"ingredient1":"buns",
"ingredient2":"patty",
"ingredient3":"tomato",
"ingredient4":"lettuce"

};

Note how nesting is used to show a heirachy.

Okay, so now we have arrays and objects, either of which can be put in place of a single value. JSON simply combines all of this in a neat back, replace values with objects or arrays when necessary.

Here’s an example:

myLunch = {

"type":"hamburger",
"ingredients": [

"patty",
"tomato",
"lettuce"
]

};

Note that the first value in the object (for”type”) is only one value, while “ingredients” has an array in the same place. This can be expanded as such ad nauseum.

The idea behind JSON is that it is extremely lightweight (consider the parallel it would have in XML), permanently stable (so no versions), and is easier for computers to understanding. What’s great about it is that conveys very easily how objects can be contained in such structures. For example, if I had a file that belong in a folder (I use the folder example because it’s the most common hierarchy that we encounter in computing). Now, say I have two different folders that this item belongs to. So where do you put the item? The answer is in neither place; rather, you’d link it to both folders. So it’s not that our JSON structure would be follow folder structures, but rather it would be based around the items, which would list their locations. Overlapping hierarchies are a concern when you’re thinking of computing’s outdated physical metaphors; the sort of tree model that you find in XML and JSON is not actually constrained as a “box within a box within a box”.

In my last post, I included a link to a page with e.e. cummings poetry, with the text “e.e.cummings” being the only text for the link. As I thought about whether a deeper explanation was necessary, it struck me how wonderfully open-ended the choice was. While there may be  best practises, there are no rules on how I can and cannot link, meaning that I can do it as is appropriate to me.

I’m fascinated  by the nature of an HTML link because, well, it’s not really a “link”. A web link is not a relationship, it’s a reference. It simply points to a different location, with no obligation by the referred to reciprocate. It’s not elegant, but the simplicity has it’s own beauty. The Internet has grown exactly because of such lenience, giving more control to users in developing the web.

Ted Nelson’s Xanadu project, a complex early hypertext system, attempted to show relationships in a reciprocal fashion. Links in his system would have been would be more uniform, predictable, and never broken. Our lesson can be seen in how the relatively archaic World-Wide Web left Xanadu in the dust. However, we still see relational links where they are more appropriate: social networks and social networking sites. A link to a friend is two-way. Thus, to sever a tie, two friends are lost, one on end of the relationship.

Thinking about this freedom, I’m reminded of Myspace. Early in its history, an exploit was discovered that allowed Myspace users to inject custom CSS. Rather than patching the “problem”, Myspace left it alone, neither stopping nor encouraging the practice. What resulted is that the site hit a chord with youth users, this exploit feeding the tendency of content creation that today’s teenagers have (see “Teen Content Creators” from the Pew Internet and American Life Project). This little site design huccup has often been cited as a reason for Myspace’s growth, while other early social networks floundered.