One “tool” for study that I have adopted are videos (mostly from youtube). I am not using these videos for finding a specific piece of information on this or that topic. E.g. a mini lecture explaining this or that. I am mostly listening to authors (e.g. Noam Chomsky or David Bohm), which have touched upon topics of my interest (e.g. A.I. or dialogue). On several occasions videos have constituted a complement to readings – past or ongoing. So, I have listened to Bohm’s seminars while reading his book on creativity and the one on dialogue.
In a video you may find something that you don’t usually find in books, which represent more like a final product. Whereas in a talk or, even better, in a conversation or interview, an author has more time to elaborate. So, it’s kind of less regimented. However, I kind of feel that reading a book is still the best way for me, especially if I want to ponder on a certain idea of concept. It’s basically when I keep going back over and over again on something in particular. That’s why, after I watched a video, I’d like to have, for example, the transcript of those passages that captured my imagination. Or that I would like to ponder. In some cases YouTube provides that, but the problem is that the way in which the transcript is visualized is not the best. It’s just this text that keeps rolling as the video unfolds.
One thing that I have been experimenting with is to watch the video (or those that are theoretically “dense” of meaning) using a video editor, which basically allows me not only to pause the video, but also – and more importantly – to edit it so that I can cut out the segment(s) of interest. For some reasons, this helps a lot. I may watch a single segment for 5 times. Sometimes I also create my own little video only with the parts that I want to focus on and share it afterwards on youtube, facebook or instagram. But that is, say, the final product and it’s in a way already “processed”. Which means I may cut off those parts that would be helpful for making another person understand the concept or the idea.
Indeed, the whole thing is time consuming. The interesting thing is that it seems that reading can be very selective the way a video cannot be. I can skip parts of a video, but not as efficiently and efficaciously as I do with a text. The best thing is to find the transcript on a website. This happens with some of the big names (the video below is an example).
I’ve been recently thinking that uncertainty is in a way a more interesting category that ignorance (although they are indeed closely tied to each other).
When we say “I don’t know”, what this may mean is not that we lack knowledge. What we lack or might lack is certitude. In other words, we are not sure of something (which we might know). This is a very interesting statement, because uncertainty – unlike ignorance – cannot be overcome by getting to know that which we ignore. That’s an illusion.
Uncertainty is primarily ontological. It’s about how reality is “structured”. So, we cannot overcome it. That’s what neurosis does. But that’s the interesting part of it. Because what uncertainty requires is not an epistemological act (I try to know what I ignore), but ontological. Or, better, existential. It concerns our existence, the very fact that we exist and live in this very world.
So, we can in a way still think that we “overcome” uncertainty, but that comes as a resolution. We resolve to act in spite of the fact that we lack certitude, that we cannot be sure as to where we are heading to.
In his Uncertain education Mark Johnson argues that technology – among other things – increases the availability of options and consequently it makes it harder to choose which one to select. Which implies that uncertainty increases.
As a researcher, this is what I feel I am currently facing: what should and could I use to share my research? Which option?
Very often the constraints of the medium that we use to share our idea affect the very way in which we can share. For example, until the advent of the web, two options were basically available: writing a book or an article. Indeed, other options were available too, but only to a restricted minority, essentially those who would be featured, for example, in educational documentaries or films. Sometimes lectures were video-recorded, but these did not really have large circulation. And indeed, the publishing was mediated by institutional arrangements, which often reflected power relations within the academic institutions themselves.
In 2019 the options of sharing go well beyond those granted, say, 30 years ago. There are different media that a researcher can exploit. There are podcasts. One can have a YouTube channel, a blog, a twitter account. Or facebook (I am one of those who shamelessly use it). We can record our lectures, the conversations with colleagues, etc.. Along with the different media, there is, though, another option, which brings the discourse about technology and research to a totally different planet. I am talking about sharing the very process of researching along with its chances, uncertainties and the rest of it.
When we talk about sharing one’s research, we immediately think of sharing the results of this or that study. And this can happen via different media. Now, very often sharing the results of a study implies to ignore, if not suppress, the process behind it. Especially when publishing in the so-called scientific journals, what we are kind of forced to do is to create a sort of fictional story, which re-describes the whole process that led us to the results being presented. We essentially sanitize the research process and we end up presenting only those elements that would fit in with what is prescribed, that is, some kind of version of the so-called scientific method. So, we pretend that we have begun with finding a gap in the literature, which led us to the formulation of a research question. Then we say that we have moved on to designing the study – how we decided to conduct our experiment or the data collection. And finally we present the results answering to the research question that we posed at the very beginning. No roundabouts events, No surprise. Just rigorous and methodical if not algorithmic application of scientific rationality. Indeed, we all know that this is never the case. Especially when we don’t consider normal science, which is essentially that part of research devoted to puzzling solving that simply builds on top of what has been already researched.
Now, suppressing the very process of researching implies that we simply misrepresent the scientific enterprise, because the retrospective look that we inevitably cast upon what we have done deforms it. But even more importantly, by sharing and communicating only the end-product of research, we lose the chance to share with others the very engine setting in motion the world of science and research, which is research as a thinking process that happens in a dia-logical way.
So, coming back to our publishing tools and to make a trivial example, we can indeed make a video where we present the results of our research, how they are relevant for the survival of our species and other blah blah. But we can also share our research while we are still researching. This goes well beyond the mere popularization, which, sadly, implies that there are experts, on the one side, and the rest of us, on the others. Opening up the process of thinking implies sharing via dialogue and conversation. Which is a powerful tool for the democratization of science and research.
Two weeks ago I participated in a hackathon organized by Riga Technical University within the framework of FuturICT 2.0 project (Large-scale experiments and simulations for the second generation of FuturICT). During the hackathon I delivered a talk on educational data. The goal of the presentation was rather modest: I wanted the folks there to reflect on the way in which we engage with data in education. Essentially, the message was that we manipulate data and we can do that in different ways depending on the type of inference that we are drawing “from the data”. We can look for confirmations, similarities among phenomena or explanations. I pretty much referred to Peirce’s well-known classification of inferences – deduction, abduction, induction. I did not talk about a forth category, which is retroduction.
After the presentation, I started thinking about how to present these four types of inferences or, better, epistemic activities (activities that deal in one way or another with knowing). I usually use the syllogism to explain the differences, but I was looking for something simpler.
Let me start with deduction. What do we do with deduction? If deduction is used theoretically, it generates predictions that are meant either to confirm a hypothesis or not. It does that by deriving the consequences that logically follow, when we assume that something is the case. From example, all men are mortal. And if I am a man, then I am going to die sooner or later. When it’s used practically, deduction prescribea certain course of action. The prescription derives from subsuming a case under a general rule. For example, when one is ill, it’s better to stay at home and rest. This means that, if I happen to be ill, I should not go to the party.
So,
deduction predicts or prescribes
Induction is a different matter and perhaps a bit more complicated. Or not so straight-forward as deduction. Here I have to start with an example. Let’s say that I meet a thin man and I discover that he is a runner. Then, I meet a second man who is thin and who is a runner. Then, I meet a third one, who is the same as the previous two. After a few observations I start thinking that runners are thin. In doing so, I am taking single observations (e.g., the thin man is a runner) and I generalize them. Such generalization – it is very important to note – can be quantitative or qualitative.
Qualitative generalizations are based on numbers. So, I say that I verified that 80 out of 100 runners are thin. Then I generalize that this is true for the entire population of runners in, say, Tartu, which is the city where I live. If in Tartu there are, say, 1000 runners, then I can draw the conclusion that 800 of those are thin.
Qualitative generalizations are different and in a way more interesting. Let’s say that there is a certain disease D that manifests itself with a list of symptoms a, b, c. By making a inductive generalization, I attribute those symptoms to any single instance of that disease D. So, all cases of that disease will have the set of symptom a, b, c.
It’s important to note that induction does not really produce new theoretical knowledge. It allows us to see things as similar. So, it seems that induction is essentially related to making similarity judgements. This can be done independently from the number of observations. If a boy touches the hot stove with his fingers once, he will inductively derive that the next time he does the same, he may expect something similar to happen. So, the generalization that the induction does is related to similarity.
Induction generalizes and finds similarities
Now comes abduction. Abduction is often mistaken for induction. The example that I have just made is perfect to show the difference with induction. The boy touches the hot stove and he burns his finger. He may assume that the next time he is going to do the same, the same will happen, because the second time will be similar to the first time. However, induction does not tell us anything about the finger. This is done via abduction. When the boy touches the stove, he perceives that his finger burns. That is done via abduction and it deals with the recognition that somethingis the case. In this case the recognition happens at the perceptual level. It’s performed, in other words, by our senses. Abduction performs an act of synthesis, as it brings under the same umbrella different elements. To make an other example, when we see a face, we don’t see the single individual components of that face. We see the face as a whole, which is in this case a perceptual synthesis of all the elements composing it (most of which we cannot really name). So,
Abduction recognizes wholes
Now comes the last in the list: retroduction. This is still the trickiest of all. So, what I am sharing now is very tentative. In general, I tend to see retroduction as a sort of bag containing different molds. For example, I am not a wine expert. And if you give me a glass of wine, I can’t really say much about it. I can essentially say if I like the wine you give me or not. But if you take a wine expert, he or she will tell you way more than me. Now, we both can recognize that something is wine and not coke. The reason is, to put it simply, that we both have the category “wine” and “coke”. What we do is that we are able to create a synthesis of all the clues that are coming to us when we drink something. However, the wine expert has way more “categories” than me. That’s why he or she recognizes many more differences. It’s a little bit like the doctor. The doctor has a sort of library of diseases, which allows him to make sense of the patient’s symptoms. We can call them symptomatologies. Now, retroduction answers to the question concerning the development of those symptomatologies. So, retroruction does not tell us that this is pneumonia or Lambrusco wine. Retroduction provides the “molds” to then capture things in the world. Or, better, to take them off from the background and bring them to the foreground. So,
I have finished to read a book that I warmly recommend: Keynes. The Return of the Master by Robert Skiedelsky. In the book there is a section on Uncertainty, which turned out particularly insightful for me. What Keynes realized – unlike other classical economists – is that uncertainty is always an issue because our livelihood or prosperity depends “on our taking a view of the future”, as Skiedelsky put it. A page later Skiedelsky quoted the master himself from his General Theory:
We simply don’t know. Nevertheless, The necessity for action and decision compels us practical men to do our best to overlook this awkward fact and to behave exactly as we should if we had behind us a good Benthamite calculation of a series of prospective advantages and disadvantages […].
As Keynes understood, the category “uncertainty” is a tremendously relevant one in order to make certain phenomena belonging to the social realm a bit more intelligible. That is to say, we may see certain phenomena in direct connection to uncertainty. Or, if you will, an attempt to handle the very fact of uncertainty. Here I would like to talk about the status game and the current situation in the so-called academia.
Keir Martin and Thomas Hylland Eriksen from the University of Oslo have recently published an article (which you find here in English translation) dealing with the situation at their university. There is a passage summarizing the present situation, which seems to be afflicting many of us around the world:
a tendency to view the world as a problem to be fixed with spreadsheets leads to the prioritization of hitting the numbers over the nurturance and development of the human environments that those numbers were intended to measure. It is a tendency that if left unchecked can cause immense problems.
Now, several explanations have been advanced, several labels have been pulled out to understand the emergence of such a tendency. I think that what Keynes wrote is extremely relevant. Essentially the spreadsheet-driven strategies and all the rest of that are an institutional response to uncertainty, which is actually the main issue here.
The first point that I would like to raise is that I am afraid that we are not quite sure of what the university (and higher education) is for. First big uncertainty. Let me focus on research here. What is research? In some disciplines people have been quite successful to get the message across that research embodies the ideal of the scientific method and therefore they see themselves as those who can reach the truth or, at least, get nearer to it. Decisions to be made should be supported by evidence and evidence is provided by science. All the rest relies on anecdotes or, even worse, biases that are resistant, yet unreliable. In other cases, research is successfully presented as fundamental for innovation, which contributes to productivity, which means competitive advantage, which means profit for the individual and growth for the state.
Now, while there is a grain of truth in what I have just described, research is way more than that. Research is not just about evidencing. Yes, research is a device that produces evidence. But the question would be then: evidence for what? That is a question of meaning that goes right to the core of the scientific enterprise, which is not just about coming up with evidence supporting certain ideas. We want those ideas to be good. And the evaluation of ideas cannot be reduced to evidencing: it is fundamentally as fallible as the knowledge we produce. If we don’t deal with that kind of uncertainty, we will start playing conservatively. We will just try to prove that something is the case without imagining other cases. An interesting piece of evidence here – pun not intended – is that negative results have virtually disappeared from the scientific publications. Which is a clue pointing to the fact that there is a strong bias in favor of “positive” results, because knowing what is false is not enough. Again, uncertainty coming back from the backdoor.
The second element concerns the people, who gravitate around the university. The uncertainty in this case regards their career. I have heard over the course of the years many people express their concern regarding their future: Will I be able to get a position at the uni? Will I be able to keep my job? Will I ever get the tenure? Will I ever be able to do research with my own team? I am also concerned about my own future, because I love my job.
The proliferation of staged competitions is the response. The word “staged” is keyword here, because what I am talking about here are essentially games like this one:
As a university employee I want to know what I have to do in order to keep my job or to progress in my career. Staged competitions reduce uncertainty, because I can now see my career as a game. Just like in any game, there are rules, score, etc. (The game also becomes a way for those on the top of the hierarchy to mobilize people’s energy and channel it into certain desired targets, but let’s leave this aside for the time being). It all becomes predictable.
What is the problem with that? To put it simply, we academics start playing the game and in doing so we forget that we are supposed to do science, not piling up pieces of papers called “A-journal articles” (which is an outcome, by the way). The less we care about doing good research, the less relevant research becomes for those who are not playing the game. Besides, when everybody starts “playing the game”, things do not necessarily become easier. On the contrary, they become harder and harder. So, uncertainty comes back again from the backdoor. That’s called performativity.
What is the problem here then? The problem concerns the sort of methods we are collectively deploying to face what Keynes called “irreducible uncertainty”. That is, it concerns the design of our institutions, which are in the end supposed to handle such irreducible uncertainty. The two examples that I have just described rely, on the one hand, on the so-called scientific method, on the other, on gamifying the process of distributing costs and benefits. Those are the “methods”. As I noted above, when we apply them, uncertainty comes back from the backdoor.
What would be the solution then? First of all, this is exactly what I am talking about: there are no certain solutions. Or, to put it another way, repressing uncertainty is not a solution. That is clear in the two examples that I presented here. Science itself is uncertain: it’s the beautiful risk of discovery. Discoveries cannot be predicted, can they? New ways of looking at the world (natural as well as social) will be found along the way, not planned out in project proposals.
Then, careers are uncertain. We don’t know who is going to be the next Einstein. But perhaps we should just try to provide a good work environment to help the expression of people’s creativity and imagination. That would be a good starting point, instead of coming up with all sorts of pseudo games.
As a wrote in a previous post, I am currently working on a paper with Tony Tonni – educational technologist at Eller Music College here in Tartu. The text we are composing is supposed to provide a case study, in which we illustrate how a few ideas coming from cybernetics (notably, from Stafford Beer’s work) can be used to understand the role of a educational technologist in an educational institution at different levels of abstraction – that is, practically and theoretically. The case study is built around Tony’s challenge to handle a videoconferencing system for remote regular one-to-one music tuition, which involved a pair composed of an accordion student and his teacher. Essentially, what we are trying to show is that the educational technologist is a variety-handler. And we do that by providing “ethnographically rich” examples coming from the field.
The main thesis that the educational technologist is (or should be) a variety-handler is meant to be something practical. In other words, we would like to show practically what that means. There are also some broader implications that concern the way in which we see technology. And that’s even more exciting. The kind of narrative that we propose in the paper is essentially based on the idea that technology (or I should say technological innovation) provides us with new options for (the organization of) learning and teaching). And that’s called variety. The proliferation of options (another key term) may turn out to be challenging for the educational institution (a school, for example), because it essentially means that its members can do things in (many) different ways. However, the proliferation of options does not automatically translate into something good or better. Conversely, it may create confusion and a lot more work to do, which may overburden the people in the organization and distract them from their core business – teaching and learning. Essentially, more options means more decisions to make, more tools to test, more of everything. That is not necessarily bad, yet…
Within such a scenario, what should we do with technology? In a recent post Mark Johnson writes that we should look at the system “people plus technology”. To do that, though, we need a meta-system, which is essentially something outside of the system. Now, according to Mark, the problem is when we put technology in the meta-system “to amplify the uncertainty mop” (uncertainty here refers to the fact that we are not sure what option is better than the others). This prompted me to have two kinds of thought, which reflect the duality of the system, which is partly technology, partly people.
The first is that we try to deal with the effects of technology with more technology (read “A.I.”). This is what Alan Watts called “the competition of consciousness”.
In other words, we are trying to match the proliferation of variety by proliferating even more variety. Which eventually means confusion (see my other post). The other thought is that the technology in the meta-system may mean the reduction of people’s ability to express their own variety, because the technology becomes the variety mop that Mark is alluding to in his post. And this essentially means the reduction of variety where we actually don’t want to see that, provided that we all agree that the human potential is something to nurture, indeed, not to destroy.
Let’s go back now to the meta-system. What is that then? Our brain? Is it us – the people? A new form of organization?
I am currently working on a paper with Tony Tonni – educational technologist at Eller Music College here in Tartu. The main idea is to show what practically means to say that an educational technologist is a “variety juggler. The label “variety” and all the theoretical jargon we are using comes from Stafford Beer’s work, which we are trying to apply to a specific domain – that of educational technology.
In writing the paper, we have stumbled upon an important issue, which I will try to summarize here. For example, for a teacher, the appearance of new technologies potentially provides new options for doing one’s teaching. This translates into more variety to handle from the teacher’s side. So, a teacher can use this tool to do this, that tool to do that. And if she or she decides to involve students in doing so, we see the exponential growth of possible combinations and re-combinations. In Beer’s terminology that is called “proliferation of variety”.
Beer’s take on variety (From Designing Freedom):
Now, the problem is what having more options actually means. Or what it may actually come to mean in different situations. One idea we have started playing with is that variety may mean that we are dealing with “high-variety” situations, that is, situations with a certain degree of complexity, which, in turn, necessitates the adoption of strategies that are equally “high-variety”.
However, Variety may also mean that things have gotten a lot more complicated. So, we should probably make a distinction between complexity and, say, complication. One way to put it is that complexity is not chaotic. We may find in it a type of organization, which is simply more complex than others. Complication seems to point to a situation in which things went astray. It all became a big mess. Indeed, complexity is sometimes perceived this way. We may get really confused. However, complication seems something different. And this is of pivotal importance for educational technology. Is the proliferation of options (that is variety) something that makes things more complex? Or just more complicated?
