unc∞rdinated

A Quiet Project. Last year, the chief prosecutor of the International Criminal Court lost access to his Microsoft email after the United States sanctioned him. He had to move to a Swiss provider. The court, seated in The Hague, has since announced that it will replace Microsoft software on its workstations with openDesk, an open source productivity suite of German provenance. For Europe's governments, this cut-off was the moment a possible, distant risk became decidedly less abstract. For years the worry about American technology had been about access: the US CLOUD Act lets American authorities compel American firms to hand over data wherever it is stored, and Microsoft has conceded it cannot guarantee otherwise. The ICC episode was about something else. It showed that a foreign government, acting through a vendor, could switch off a public institution's tools. One might expect alarm. Instead, there have been quiet moves, driven by a mix of cost, strategy and geopolitical uneas...

  “Black October” is the name some people are using for what is reportedly a series of AI-driven cybersecurity incidents, still unfolding as I write this. It is an emerging phenomenon in the most literal sense. The events are still happening, the name hasn’t settled, and nobody quite agrees on what counts as part of it. Or if it is happenning at all. For something this new, social media is in a perpetual state of fog of war. Posts arrive before the vocabulary does. And if you try to find them by keyword, you hit an obvious problem: “black”, “October” and “AI” are some of the most ordinary words on the internet in autumn. Search for them together and you get Halloween art, festivals, and a lot of general hand-wringing about AI, with the posts you actually want scattered somewhere inside. Using this as a way to think through what searching semantically rather than just searching lexically might look like in practice. First, go wide to get a broad corpus I searched the AT Protocol net...

  I should have been working on my last intro programming lecture for my Programming, Algorithms and Data Structures class at CBS. Instead I spent time thinking up an obscure reference to the 1970s Anne Lund "Atomkraft? Nej Tak" logo that most students in my class would not get. Not even many of the Danes, I think. Anyhow, its my visual manifesto for less CS concepts slideware and more getting your hands dirty in Data Science courses. As I've said before, I prioritize beautiful insights over beautiful code . 

For the last 3 weeks, I've been teaching into the course Programming Data Structures and Algorithms , part of the Masters in Business Adminstration and Data Science program at CBS. I teach the first six weeks of the course, which is essentially intro to Python programming. Its been an eye opening experience. Not least because its the first large class I've taught in well over a decade. Around 170 students, almost the entire cohort of the program, and for many, their first ever masters class, and for some, their first ever programming class.  I've been fucking around with code for years, as the (two? three?) regular readers of this blog will attest. But the fact remains for the last seven years I have been, by intent and design, a qualitative scholar in the interpretive stance. And most of what programming I know is self-taught, unless you count the two weeks of R I did during Lance Waller's PhD seminar in ecological models at Emory in the late 2010s, and the handful o...

Holy moly, I can barely believe it, I'm an actual assistant professor now.  Surrealness nothwithstanding, I have to engage with my new chosen academic field of IS/Information Systems. And so I've proposed a TREO talk at ICIS 2026  in Lisbon called " Computationally-Entangled Interpretive Methods: Using Computation In, and In Service Of, Interpretation. " ICIS is the flagship IS conference, and a TREO talk is short, five-ish minutes, an opinion piece more than a paper. That format forces compression, and compression tends to leave a position under-explained. This post is my attempt to say more plainly what I mean, mostly for myself. The starting problem is size. Organizational and social life increasingly leaves traces in corpora, digital trace data, social media archives, news archives, that are too large to read closely. A researcher used to sitting with transcripts or field notes now faces millions of documents. The compounding problem is the tendency in my fie...

  What was once my living room in Montreal. I fly to Copenhagen tomorrow, and with that I wrap up 20 years and change as a resident of North America. I came here in January 2006 as a 20 year old undergradutate transfer student to Emory University in Atlanta, Georgia. Much and more has transpired since then. Family, career(s?), alphabets after the name. I leave behind in this continent things made of iron and stone and wood. I leave behind language in silico. I leave behaind self-perpetuating patterns of behavior.  I leave behind familiar things.

Sitting on my bed typing this, my apartment is in disarray. The movers come tomorrow, and everything we are taking with us to Denmark (aside from our suitcases) is packed into 22 small cartons, 9 plastic tubs, and 1 garment case. Books, stationery, clothes, a few toys, some mementos, coffee equipment, cast irons and Indian stainless steel and brass kitchenware. This is 20 years (13 for Aditi) of stuff we have accumulated in North America that we will take when we go. Reflecting on what w leave behind, I notice furniture, beds, linens, clothes, toys, almost everything in the kitchen, so many electronic devices and appliances and gadgets, so very many plants and ceramics. I remember that I left my car behind in Atlanta, 7 years ago. Headed to Copenhagen next Friday with Lara. Aditi will follow in a few weeks after getting in some more fieldwork. We have an apartment there already, and Lara and I will feather the nest in anticipation. I'd like to explore the city with her, maybe take ...

I'm working on an empirical project with two co-authors, where a significant part of the central phenomenon we are studying unfolded online on twitter.  We have a massive multi year tweet and factiva news corpus that we sense-make together by periodically meeting to discuss the data and to theorize qualitatively from algorithmically generated representations of and algorithmically sampled texts from the corpus, previously drawn models of the phenomenon, memos, manuscripts, etc. We each of us have varying familiarity/expertise around the special phenomenon, the substrate upon which it unfolds (online discourse), the theoretical/research conversation in which are participating, and NLP. In collaboration, we see things.  I'm currently working on creating a visual representation of key themes (based on a BERTopic model) in the corpus, in particular the twitter discourse, over time and across our own interpreted actor categories. An interesting macro pattern emerged in convers...

What is AI? I've written about this before , and given that it is an emerging technology, I understand that there are many conceptions of AI. In this post I'm referring specifically to generative AI. However, after having reviewed the Nth management/IS manuscript that treats genAI as some amorphous blob that does everything and nothing, and learns from everything and nothing, I decided I should try and break it down into components. My emphasis here is function and modularity of the various components that come together to perform "genAI". Will keep iterating on this for a class Samer is teaching.

I've been asked to offer some thoughts on fieldwork and data collection at a BricoLab meeting tomorrow, thought I could use this space to ruminate.  Not everything one comes across during fieldwork is "data". I think qualitative fieldwork is incredible because it produces profound knowledge about settings and phenomena. If one truly knows something they can write conceptual or theoretical pieces about it all day long. However knowing the phenomenon is not the same thing as having data about the phenomenon . And I am ultimately an empiricist and I need data.  I also want to recognize that its not as simple as data/not data. Its not a binary. I believe there is a continuum of data-ness of knowing from qualitative fieldwork, along the dimension of reflecting + encoding.  Let me try to explain what I mean by that... You come out of a session in the field, an observation session, and interview, whatever. You are buzzing with excitement about what you have discovered, yo...