Open Source Alternatives

I don’t know if it is possible at this point in time to avoid working in Microsoft applications. Given how many organizations now have some version of Office 365, it seems like all outputs are eventually a Word document, a PowerPoint slide deck, or an Excel spreadsheet. But those are outputs, not throughputs, and I don’t think any of those applications is terribly good to think with. Communicate to/with others who are used to things being packaged in a particular way? Okay, fine, here’s a Word document.

The process involved in getting to an outcome should be more flexible, more tuned to how individuals and teams work. For the record, Teams works well enough, but OneNote is a disaster. Personally, I use Devonthink and Scrivener for the iterative process of bigger projects, but neither of them is team-friendly. And neither is really all that iPhone-friendly, really. They have iPhone apps, which are really quite functional, but the true power of both apps is really best experienced on the Mac. (I only use two devices: a Mac and an iPhone, so the iPad version of both may very well be an incredible experience, but I decided a while ago that my version of techno-minimalism meant I only ever worked on, and worried about, a computer and a phone.)

The problem, for me, is that collaboration needs to occur across platforms, so it looks like I am stuck with web apps. That noted, there appear to be a range of open source alternatives that look pretty good. BTW has collected over 200 open source alternatives to a range of software tools that businesses use: from note-code databases to note-taking to kanban.

Clarkson’s Farm

Let’s get one thing out of the way upfront: Jeremy Clarkson is a prig. I found his persona on Top Gear virtually unbearable. That noted, his presence in his eponymous series about becoming a gentleman farmer is entirely redeemed by the fact that he, or the production crew, are perfectly happy for his priggishness to lead to his downfall time and time again. With all the gusto of a man who has made too much money too easily and now finding himself essentially a lord of the manor, which here is 1000 acres in the English Cotswalds, Clarkson lurches from one under-considered venture to another. Not once in the first few episodes does anything go right — so much so that I have to believe that the show is built somewhat around him showcasing his own foibles.

Having blown up his house in a pre-series moment, Clarkson’s Farm begins with the man himself buying an over-sized and over-powered tractor, which is something you would entirely expect of the long-time host of a show about cars. And you certainly expect to see him in the tractor, puttering about. You do not expect him to buy sheep, and then get a bit weepy when it turns out that three need to be euthanized. This is Clarkson as you have never seen him before, and, damn it, he is quite likable.

I am still at the beginning of the series, so I cannot offer anything in the way of spoilers, except to say that if you haven’t watched the series because it has Clarkson at the help, have no fear, he is far from being in charge of, well, almost anything. As the show unfolds, other characters become more and more compelling in their heroic efforts to nudge Clarkson into reality. In fact, one of the interesting dimensions of the series is how it carefully peels the onion on the alternate reality in which many celebrities live. Clarkson really has spent considerable time with reality always bending to his will, and, here, with a 1000 acres filled with plants, animals, and people who have their own ideas about how things go, he meets his match. Or, rather, we get to see a grown man come to terms with the fact that reality really does exist “out there.”

And if you don’t give a damn about the likes of Clarkson — and, honestly, the last four years have really been rather over-filled with privileged white guys going on and on about how they know better, it’s okay. The English Cotswalds are quite lovely, and you can always turn the sound down and just enjoy the scenery.

Why I Use a Reference Manager

The process I am going to describe here is drawn from my experience with Bookends, but I am sure the functionality is available in other reference management apps as well. I chose Bookends because it’s focused on Mac users and thus its GUI is native to the platform. I am fairly certain that Zotero has similar functionality, and I may end up using it when I am on Windows (and also because on Windows I am part of a team). The process I have in mind is adding a new reference and then adding its concomitant PDF.

First, an establishing shot drawn from work I am doing now for an essay about COVIDlore. This collection is built on top of some previous work on the flu. (Somewhere I also have Zika and Ebola bibliographies, and one day I will migrate them here as well — for those curious about the library just above entitled Legends/Virality it is in fact related but more focused on the notion of informational “virality.”)

Bookends Main Window

To add a new reference, I usually use the Quick Add function, which is handily called with CMD + CTRL + N:

Screen Shot 2021 08 04 at 09 57

I can paste the DOI from the website where I found the reference, which may or may not be the originating site — it could be a reference from another paper, for example, and Bookends does all the lifting. (This works 80-90% of the time, and so it is frustrating when it doesn’t, but there is a built-in browser that allows you collect metadata for a reference quickly.)

Once the reference is in the collection, I then CMD + OPT + R to fetch the PDF to the reference. (If you have already downloaded the PDF, you can use CMD + OPT + P to attach it from a local source.)

Screen Shot 2021 08 04 at 09 57

That’s it. The PDF is now in that particular collection as well as in the main library. Since the PDF is sitting in a particular folder which I also have indexed by DevonThink, I can take notes in that app, which will create an annotation file just for that purpose.

Automating Text Cleaning

I am fundamentally ambivalent about the automation of text-cleaning: spending time with the data, by getting unexpected results from your attempts at normalization strikes me as one way to get to know the data and to be in a position to do better analysis. That noted, there have been a number of interesting text-cleaning libraries, or text-cleaning functionality built into analytic libraries, that have caught my attention over the past year or so. The most recent of these is clean-text. Installation is simple:

pip install clean-text

And then:

from clean-text import clean

The clean(the_string, *parameters*) takes a number of interesting parameters that focus on a particular array of difficulties:

Where I Want to Live

Ostensibly an essay on urban planning, and how Houston doesn’t plan for walking, Not Just Bike‘s “How I Got Into Urban Planning (and Why I Hate Houston)” is really an exploration of how traveling to, and spending time in, different places can open your eyes to what you think of as given and how it can lead to you choosing something different. It could be argued that every society gets things right and gets things wrong, and if you are lucky, you will, or get yourself to, be mobile enough to view a selection of societies and then choose the one that fits you best. But what if you are not lucky? What if you aren’t mobile? What if you are stuck? I think too often, and this is one of the subtexts of the essay (I think), we do not recognize that poverty isn’t a choice, but a lack of choices. How societies strive, or whether they strive at all, to give everyone within its bounds the ability to make fundamental choices is probably a better measure of its health than many other metrics we use.

Of Mollusks, Matrices, Modeling

In the Science Museum of Minnesota this past weekend, I found myself pouring rather closely over the mollusk exhibit, if only because I could not fathom—pardon the pun—why there was such a grand display of them. I didn’t have to look far: Mollusks are often the leading indicators that an environment is in danger. The museum is home to a larger collection of mollusk shells, many of which reveal where mollusks once thrived but are now scarce or non-existent thanks to changes in the landscape brought about by agricultural or industrial contamination.

Leading indicators is, of course, quite popular right now, because a lot of people are interested in being able to predict the future based on the kinds of early successes we have had with machine learning whereby algorithms trained on a reasonably large dataset can discover the same patterns in new data. I am reminded of work reported by Peter Brooking and Singer on XXX as well as the “calculus of culture” being developed by Jianbao Gao.

What you would need of course is a reasonable definition of the parameters of your “socio-cultural matrix.” The social dimensions would be all those non-text data points that might be of interest and associated with humans either individually or collectively. The cultural dimensions would be texts and other discernible behaviors, again either described individually or collectively. We know this is possible because, to some degree, Cambridge Analytica has already done it, and we can be sure that other organizations are doing the same and just not talking about it. (In a moment when all this data is available, either by cash, hack, or crook, you would be a fool not to collect it all and compile it as many ways as you can possible imagine, and then some.)

Breaking off some piece of this larger matrix, or set of matrices, is something we all need to be better about doing: modeling complex environments is something that needs to get taught and practiced more widely—all the while reminding people never to mistake the map for the territory. To some degree the social sciences do some of this at the computational end, but the kind of statistical, and sometimes speculative, modeling suggested here is not as pervasive in public discourse as it should be.

Connective Environments

I have wondered for a little bit now how social information systems fit within the Army’s MDO conceptual framework. I understand that they are part of what the Army considers “information operations,” which has a variety of dimensions to it, from OSINT (open source intelligence — basically scraping the web and other public sources) to information warfare. What I have not understood is where information fits within the conventional MDO framework, which seems very tied still to physical environments, with cyberspace representing a non-physical, thus “virtual”, environment.

It would appear, from a recent briefing, which is entirely in the public domain that information is considered a connective environment, like the electro-magnetic spectrum. You would think that cyber might be considered a connective environment — just as land, sea, air, and space are really connective environments — within which certain kinds of operations take place. But the reverse is true?

I’m scratching my head on this one.