I often say to students ‘describe, then analyse’. Well, how do you know which one you are doing and what the difference between them is? And while we’re about it, what’s the difference between method and methodology, hmmm? There is really no fundamental difference. Description always involves a choice of terms in which to describe, and these are analytical choices. Analysis is just being aware of the difference, then deciding what elements to focus on, and drawing connections between cases in order to make inferences. Analysis is therefore that ability to connect the description to a category or process that tells you about it, and being aware of the context in which it is produced.

For example, I research banknote counterfeiting. One aspect of that is looking at how banks and tellers detect counterfeits. A start to that is by describing the equipment and used (e.g. ultraviolet lamps) and then the process (the different kinds of examination notes undergo). Analysis is looking at what informs the design of those technologies and processes. One ground level assumption everyone works with is that there is an objective way to tell the difference once and for all. Interpretation looks at what effect these assumptions have in the world. For example, if tellers are held responsible for accepting counterfeits then that shifts the responsibility from the designer to the lowest level human in the process. That tells us about who has power over money. We can then say a lot more about how cash notes fit into the economy as part of a whole process based on distributing trust and responsibility. We might also look at the changing design of notes as partly symbolic, incorporating banal nationalism at some stages, and also about shifting understandings of the role of cash and money in the economy.

The design of the Euro notes gives new meaning to both. They are intentionally banal. Each country is only represented in the serial numbers of the notes, and the images uses are intended not to refer to anything that might get people hot under the collar. The security features need to be readable to the human eye and sensitive to touch, to make them quickly recognisable. We can then ask what this tells us about the notes as circulating media or as stores of value, the intended rapidity of their circulation and how the designers understand where they will be exchanged. Analysis then leads us to further questions. Do central banks plan there to be a perfectly unfakeable note? How do fake notes affect people’s faith in money? Do times of hyperinflation or deflation change this relationship? How does computer fraud change the faith people put in digital versus physical currency? Analysis also helps us tell the difference between banally obvious statements that still need to be made (e.g. that every banknote is an act of faith) and propositions that can be tested (e.g. people automatically have more trust in higher value notes).

We often think of description as mundane and concrete and analysis as showy and abstract. The reverse is true. Every act of description is an act of creation, and every act of analysis brings that creation back to earth.

<img> ‘Motherfuckin’ Critcal Thinking – How does that work?InsaneClownPosse.jpg'</img>

A regular comment on students’ work from essays to PhD drafts is that they could improve by adopting a more critical perspective. Martin Booker explores it very effectively in his blog, Critical Turkey. He defines it as common-sense scepticism plus social science analysis. Critical thinking is at heart about developing your voice on the topic and finding what matters within it. It is evaluative, considered and objective. It minimuses what we have to take as read and maximises persuasion through argumentation. Should be simple, right?

So what can we tell students to actually do to get there? What tasks can they incorporate into their thinking? My answer is that you do this by asking questions of the topic. Robert Ennis (1993) lays out a combination of activities, abilities and stances that we look for as evidence of critical thinking: ‘1. Judge the credibility of sources. 2. Identify conclusions, reasons, and assumptions. 3. Judge the quality of an argument, including the acceptability of its reasons, assumptions, and evidence. 4. Develop and defend a position on an issue. 5. Ask appropriate clarifying questions. 6. Plan experiments and judge experimental designs 7. Define terms in a way appropriate for the context. 8. Be open-minded. 9. Try to be well informed. 10. Draw conclusions when warranted, but with caution’. These roughly divide into: develop your own position from the evidence, be aware of what you are saying and and assess claims independently of what is in front of you. Critical thinking is also traceable. Yourself and your readers can grasp why you arrived at your argument because you lay out the evidence trail that took you there.

In this post I drill down further to set out some tasks students can add to their toolkit and which show critical thinking at work.

Feb 13, 2022

One part of the academic skillset I have always thought made you a real academic is the ability to argue in wholly abstract terms. I have no idea how to do that and I never shall. I can talk about specific cases but debating scientific progress as characterised by Kuhnian paradgims vs Popperian falsifiability – no clue really. It works in this context, it does not work in that one, is about as far as I get. Actually I do have an answer to the Kuhn v Popper debate and it is that both are wrong.

I am saying this because I usually say that everything comes down to specifics. Every question I can think of is at its heart about morality: what we value and why, and espeically what lives we value and how they can be lived. Questions about allocating health care resources at the end of life, developing genetic modification techniques, the viabilty of cryptomining,  and about a billion others, none can be successfully answered without invoking morality. It is always there no matter how much we like to pretend there is a rational, value neutral way of approaching the topic.

Whenever I pose an abstract question the only way I can work with it in my mind is to immediately connect it to what it means in relation to a specific, available choice. Accoring to finitism, all that any conceptual distinctions can refer to are finite sets of positions. The question of is it legal or not, becomes is it in the category a specific crime or not, becomes in what way is this particular act particularly illegal, which then becomes part of the whole set being referred to. See also any other distinction you care to mention. It is hilarious that anyone imagines that they can train a machine to spot disinformation. Every claim about disinformation comes down to something contingent: it’s correct but wrong to say it in that way, or it’s wrong but we were right to say it. Then again, no boundary is quite as well drawn as one that involves jail time for someone.

There is a way through which is to constantly move between the specific and the abstract in practice and pay attention to critical decisions which make sense for participants. Mackenzie (2022) outlines this when discussing the criminalisation of spoofing and the problems distinguishing it in the minds of traders from ordinary trading practice. Spoofing is the act of bidding up a trade without intent to buy. Mackenzie argued that this previously normalised activity was criminalised with little effect initially. It was felt to be impossible to prosecute outwith a case referring to broader market maniuplation. It became actively criminlised following the financial crash, and after the first jailing markets suddenly became very good at spotting and punishing an activity they had previously claimed was impossible to identify. Presumably traders also managed to toe this line effectively when they saw what was on the other side of it. Arnoldi (2016) suggests a shift in the technical materiality of markets was critical to the sudden spate of spoof-shaming. Algorithms had to be protected in the name of the intergrity of the whole market. Today’s tip is to pay attention to each decision being taken as one of a chain of decisions that is always addressing that central question: what matters in life, and how can it be lived.

The best software is accessible, extensible and community supported. However because reasons we tend to end up with tools that are centralised and ‘heavy’ – they do too much, much more than you will ever need, they have high lock-in and can’t be easily adapted or customised, they are opaque in their design and their focus is decidedly not on education. It would be great if someone could revive something like Apple’s OpenDoc idea for academic documents. This is where the sciences are miles ahead of the humanities and social sciences and why I think all first year students in all disciplines need to be taught how to use R. This post is very Mac focused, though most of the software I mention is cross platform.

Apps I don’t use and probably would if I was just a bit better organised

There are loads of productive apps I try and never use. Top of the list is the To Do app/task manager. I have lots of lists of plans in many apps sitting on various devices, untouched and forgotten after the second time I used them. Also note-taking apps. I use the mac’s Notes app a bit as a scratch space, and that’s as far as it goes. Project planning apps have gone the same way as ToDos. The time spend fiddling with them did help me sort out what my preferred workflow was, what needed to go where, what had to sync with what, what could be automated, and so on. There are plenty of task mangers to give a go when you need them though and they are useful for large group projects. I really should use LibreOffice instead of Word but totally fail to.

Apps I don’t use and wouldn’t use if using them was the only way to hold the space-time continuum together

Grammarly. The Autotune of writing. Use if you never want to develop your own writing voice, and also if you like everything hyphenated. Also the app installs with start at login enabled by default and is impossible to turn off. Leibniz once said there was no pure evil in the world. Leibniz was wrong.

A certain bibliographic database manager, sounds like Bend Vote.

Siri.

Apps I do use: Research and teaching

Zotero is the best for managing your readings and retrieving articles. At the moment you need the beta version to get the full benefit of inline pdf reading/annotating and if you want to save from Safari.

There are several writing programmes that are designed around academics needs such as Scrivener for long documents and various ones for focused writing such as iA. I’ve not yet tackled the world of LaTeX.

Several qualitative coding apps exist such as Nvivo which suffers from some of the problems I mentioned earlier. Unfortunately the R QDA project seems not to be widely used and has not been update for a while now.

Web browse using Brave for ad-free browsing. Tor for privacy and the darknet.

Use Google Slides for teaching/presenting and Google Docs for collaboration. They are shareable easily most of the time but it’s not so easy when working with people in China. Plus Google mines your soul.

For communication I need a range of apps to work with folk. Telegram is reasonably secure. WeChat is needed for working with Chinese colleagues, as many systems are blocked by the Chinese state.

Lots more to try, from Discord to Ulysses.

Lovely list of writing tools here.

Utilities

Rectangle allows for easy window resizing and layering, one of the Mac’s big weaknesses – very handy when reviewing/marking. 

Popclip for text actions such as opening selected text in a particular app. You can add various functions such as ‘add quote marks’, ‘paste and match’ and randomly cHAnGe cASe for that special ransom note feel.

Alfred for search and various other actions such as url shortening. It can be used to create short text clippings which can be inserted into your writing using a quick keyboard shortcut. I use this when marking essays so I can have a record of what issues come up frequently in students’ work and which I can draw on and adapt when needed.

This is a good guide to getting the most from your mac

Backup and security

I have a Raspberry pi running as a time machine backup and general file server/WebDAV/home cloud using Nextcloud. I also have a cloud backup using a cloud provider. There’s lots of help out there for how to get the most out of a Pi. It is also very calming to troubleshoot.

I use a password manager, Bitwarden. Very handy when you register and forget it.

Finally just a note of thanks to our amazing IT support folks who cope with all the craziness we bring to them. Do what they tell you and above all RTFM.

As part of the textbook ‘Dead White Men and Other Important People’, authored with my great PHD supervisor Ralph Fevre, we wrote some ‘doodles’ which were meant to mimic how students could take notes in a style that encapsulated the problem they were examining. The intention was to sum up each chapter retaining the core idea of the book, that it is written in the voice of a student and her peers. Each chapter was written as a dialogue through which the theory unfolded. The problem for student readers was finding the core argument in this without having to dig too much. The doodles were our solution, imagined as the narrator, Mila, summarising her ideas in drawing and words. As you can see from this example, Mila is left handed, like me. She writes on the reverse page of her notebook as left handers must do in order to avoid the spiral binding.

I wrote this because one of the most persistent and grief inducing questions I ask in supervisions is ‘what is your research question’, the slightly more honed version of ‘what’s your PhD about?’ It is unfair to ask it since I run many research projects where the ‘research question’ is the last thing on our minds. If as a team we explain to anyone else why we are are doing the work we say ‘it’s because this is a really unusual, insightful, baffling, worrying or productive thing to study’. It falls more naturally to explain why we are engaged in the work than what answer it is going to produce at the end of the day. The research questions are elaborations on that primary purpose rather than queries which somehow exist independently of it. We are driven by what it is about the issue that matters to us and what might matter to others.

For example: Just now I am studying the distribution of counterfeit currency through a cryptomarket. I am studying it because how distributors and buyers of counterfeit currency use the fake notes shows what elements of crime commission are incidental and which are necessary to the process. I am also doing it because I often characterise the cryptomarkets as ultimately benign when used for illicit drug distribution and I want to challenge that view with some trickier test cases where it hard to argue that the distribution mode reduces harm. Counterfeit currency involves immediate risks to those handling the notes and longer term potential harms in damaging the cash economy. The cash economy is now often the domain of less affluent groups who are excluded from the digital economy or have their own reasons for operating outside it. Counterfeit currency is presented by its users as harmless or as ultimately sticking it to The Man, in this The Man being the Federal Reserve. It is true that large scale money laundering is vastly more harmful and takes place through corrupted institutions or players.

In contrast to cartel scale money laundering, the kind of counterfeit currency use I study is small beans. However distributing counterfeit currency still puts the more marginal in society at greater risk. Cashiers or waiting staff who unwittingly handle fake notes might have their wages docked or be prosecuted. Smaller businesses which handle more cash will likewise be more at risk and unable to easily absorb losses. There are direct harms stemming from the practice itself and as society becomes more digitised these will increase. The process of deciding on a research question involves backtracking through what I just wrote so I can come up with a research question which encapsulates all that.

As I framed it in the title, the question is ‘why am I doing this PhD’ not ‘why am I doing a PhD’. The latter question is more existential and is often asked during the lonely hours of the night. The first question might answer the second to some extent. There is a purpose there.

The researcher’s kitbag should include several analytical heuristics you can apply to your case. These are not ready made pop up explanations. They are designed to aid thinking about why the social world looks and operates the way it does. This one is a version of Becker’s (1998) machine trick: ‘Design the machine that will produce the result your analysis indicates occurs routinely in the situation you have studied.’ It means working out what problem the institution, policy, device or system you are working with is solving. That is different from what it purports to be solving or what its designers intend.

Examples from technology design are good ones to start with as they embed solutions that might not always be articulated but are there. The Segway is a two wheeled self balancing electric personal mover. It began to be sold in 2001. It was notorious for the buildup to the launch during which fevered speculation about what it was and the impact it would have ran rampant. Without knowing exactly what it was people mused it would revolutionise urban life. The Segway itself was expensive and did not appear to solve any problem people actually had. It did not fit into any transport category or replace any existing transport device with something better. It was illegal and extremely anti social to use on pavements. It was slow and off putting to use in traffic.

We can apply the above trick to understanding it by defining the problem it actually addressed which was: very affluent urban dwellers walk too much. It would be better if they did not walk short distances and used this device instead. That was not a problem needing solved. We can then infer other effects of the Segway which would have come into being if it had taken off. We could call this the Uber stage. Uber sought like many other tech platforms to change transport regulations throughout the world in its favour. If Segway had followed the Uber path it would have spent vast amounts lobbying governments to allow its use in pavements, provide infrastructure to support it, and encouraged users to use it regardless of local rules. Then we would have a class of urban pavement users zipping along on their devices. Walking would become a highly stratified practice of those who cannot afford, use or refuse a Segway type device crammed into special lanes on the pavement while Segway users zipped past.

Some answers to the Becker question might sound a bit sarcastic, for example: the problem prisons solve is that criminals need places to pass on skills and drug dealers need a captive market. That’s just one of the answers though. There are  many other problems prisons are solving which highlight the absence of effective institutions to do their job such as warehousing people with severe mental health and substance use problems. That should give us a few clues to the kinds of problems social institutions they could be solving, those they should be solving and those they are solving. My surmise is that it is effective to examine each institution or social phenomena as if it were a device, bringing us back to the machine trick. In my understanding the device is more like an assemblage in Deleuze and Guattari’s sense, a skein of elements which are not necessarily logically coherent nor a unified whole, but which have powerful effects in the world. A drug trafficking network is a device in this sense, assembled from smartphones, dead drops, mules’ bodies, tourist towns and cheap airfares.

Summary of Becker’s tricks by Kathy Roulston: https://qualpage.com/2017/03/16/11-tricks-to-think-with-when-analyzing-data/

Becker, Howard S. Tricks of the trade: How to think about your research while you’re doing it. University of Chicago press, 2008.

Clue: No

I want to supplement some of the advice given to PhD students about their supervision team. There are a lot of guides for PhD students on ‘choosing your supervisor’. Advice falls into the following categories:  topic expertise, position in the field, and personality/working style. Do they know their stuff, do they know the ropes, and can you work productively together. It reflects the different elements of good PhD supervision. The supervisor should open up pathways for you, guide you towards productive modes of work and away from easy mistakes and mentor you in a more holistic sense. It’s a sweet combination. Not everyone is going to cover all of these elements which is why we have two supervisors.

That assumes students are going to be in a position to choose their supervisor. I am here to tell you that choices are limited and mostly students do not go shopping for supervisors in the way suggested. Sometimes  supervisors come tied to a project. Some supervisors only take on specific project types. There is not as much shopping around as implied and maybe there does not need to be.

I ask if you can choose your supervisor because it’s not very likely you will know those elements in advance. You will only know if you can work with them if you have already worked with them. In which case you have still not chosen them, happy happenstance has done the work for you. Paying attention to their position in the field is also self defeating. Someone who is so well known will have many potential PhDs wanting to work with them and might be the one doing the choosing.

When I am arranging supervision the biggest obstacle is that many academics cannot take on more PhDs. I don’t agree with the view that there is a supervisory type. Each relationship is unique and we adopt different roles depending on the needs of the project and the student. What does matter is that you are all able to reflect honestly about working patterns as you go along. Coming to know one’s supervisor is a process of coming to know yourself: their and your inspiration places, blindspots, comfort zones. That is why it is productive to think about what the PhD needs alongside what it is.