Questions about reading which are really about you, the reader, but are also about me, the writer

The kind of reading you do matters a lot to the kind of scholarship you are doing. The classic image of the scholar is someone poring carefully over a text, parsing each phrase and glossing every paragraph. A scholar isolated from the world around, unburdened by cares. I expect few academics can or do much of this close reading, though it can bear fruit. An intense reading of a text everyone refers to but everyone read so long ago that they have forgotten what is in it can be fruitful. What did Marx really say about the labour theory of value? Did he say it differently somewhere else? Did Foucault ever define what a clinic is? Questioning established common sense is a good habit and effective when you go back to the source. Be not cowed by what everyone knows.

There are many helpful guides to going about a literature review. I’m taking another approach here. These questions are a survey of reading habits and attitudes.  They are to allow you to reflect on the kind of reading you do. This is as much about who you are as a scholar as it is about the kind of research you are doing.

  1. Do you enjoy academic reading? What aspects do you like and what do you like less?
  2. Do you ever find the meaning or significance of a reading to be elusive? Is reading ever tiring or ever gives you a funny kind of distanced feeling in your brain? Is it time for a nap?
  3. Do you ever rely on someone else having read something and explained it? Do you sometimes still not ‘get it’ even after that?
  4. Do you ever feel guilty about reading? About what?
  5. What do you write when you are reading? Where do you keep your notes?
  6. Do you ever avoid primary texts and rely on secondary explanations, but pretend you have fully read and absorbed the primary text?
  7. What voice do you hear in your head when reading, if you hear one at all?
  8. Do you look at the bibliography of the text you are reading? Do you check their references?
  9. How often do you pause during reading? Do your reading aims grow faster than your reading capacity?
  10. Do you exclude readings based on titles or abstracts?
  11. What characteristics of a text do you find particularly appealing, and in contrast, are there any that are alienating? Be honest, we all hate something about the text and skip over it and hope that it wasn’t that significant or that the authors weren’t hoping you skate over it as well.
  12. Footnotes: yes or no?
  13. What’s the proportion of texts you cite to texts you have genuinely read?
  14. Do you ever run out of time to read? What do you do then? Do you ever spend too much time reading the first few pages and then have to rattle through the rest and hope it doesn’t contain any nasty surprises?

For further questions you can ask about your reading, this is a really good reflective tool which inspired my thinking for this blog post: Navigating the page. An academic guide to effective reading

Crimes of forgiveness

Forgiveness is good for the soul. Debt forgiveness is the juice that keeps the economy fresh. If all debts were honoured then capitalism would close up shop. Debt forgiveness can be  egalitarian. Social movements promoted the forgiveness of odious debts incurred by developing world governments. Debt cancellation has been a recurring plank of radical politics through history. Yet debt forgiveness can be and mostly is a tool of maintaining the hierarchy of wealth. The richer you are, the more you owe in debt, and the easier it is to have that debt written off. Owe the bank a billion pounds and they might agree to collect 15% of it. Try styling that out with your mortgage or your credit card.

When governments get involved this amounts to a kind of larceny. Firm owners with an incentive to take on too much debt and then escape it through bankruptcy will do so provided there is a cosy regulatory arrangement with few penalties (Akerlof and Romer 1993). This can be seen in the great recession of 2007-9, and at other times where debt crises produce government bailouts in the name of Keeping The Economy Working. It is a form of economic parasitism and organised crime. There is a network of companies who engage in this mutually parasitical action, supporting each other in it. Governments guarantee a vast amount of debt/obligations so it is a big sea to swim in. There is then a spreading threat of looting along with some coordination of looting activity. A key distinction which makes it looting is that bankruptcy is the strategy rather than the result of high stakes risk taking, as Akerlof and Romer put it, it is a plan to ‘go broke’ not ‘go for broke’.

The attack vectors used by firms whose business model is debt looting are: inflated net worth using accounting tricks (pretty much normal through the whole economy up to the financial crisis of 2007), using the expected yield curve (another accounting convention), and leveraging debt for acquisitions. Take on risky assets and blow the thing up. One technique is creating illusory capital through transferring ‘assets’ which are in fact liabilities. This allows the company to pay itself much more than it is worth.

The authors do not frame this as moral hazard – the worry of mainstream economists – but as massive socialisation of risk through debt acquisition. The main economic frame used is excessive risk taking. However that doesn’t capture the way in which those involved deliberately take risks they know will fail in order to loot.  Therefore we need the tools of criminology rather than economics to fully grasp the extent of the problem of economic looting through deliberate debt failure and stop framing it as just self deluded risk taking. That framing feeds into the self conception of the Masters of the Universe as unlike mere mortals. Treat these crimes as organised crime – well coordinated, planned and able to leverage insider power and political connections.

Akerlof GA and Romer PM (1993) Looting: the economic underworld of bankruptcy for profit. Brookings Papers on Economic Activity; Washington (2). Washington, United States, Washington: The Brookings Institution: 1. DOI:

Encryption and entropy

What is the physics of society? Its chemistry, its biology? We are beings who live through these material stuffs. Biochemistry matters for mind and behaviour. For example, the distribution of energy in the form of food is a critical question for health, for cultural symbolism, for life. The distribution of time is another dimension. That happens in a tangible way. Being poor often means not having much time – so much time is spend in trying to get money, to get cheap food, to try and get bureaucratic systems to function as they should. It’s tiring and low energy. Intoxicants and pharmaceuticals are distributed  in ways that promote and embed some cultural performances and undermine others. Chemistry is a mechanism of social structuring.

The laws of physics, which ye cannae change, come into my study of distributed cryptocurriences. Encryption is a technique that makes use of a fundamental feature of thermodynamics, entropy. One of the most effective entropy machines of our times is bitcoin, generator of heat and eater of graphics cards. The bitcoin model is simple: venture capitalists generously give to subsidise bitcoin miners who run their network for the benefit of drug dealers. The system consumes between 50 and 75 Terawatts per year. Digiconomist estimates it as the equivalent of dropping another New Zealand or Chile onto the ecosystem. Externalities are significant in the form of pollution. Well, every system generates entropy. Does it create order to pay for it chaos? Bitcoin’s contribution is to use entropy to create an immutable record of transactions giving currency the novel quality of memory (Maxwell, Speed and Pschetz 2017). Bitcoin redistributes time between users of the system. To get past its sluggishness you have to pay.

To an extent this is restating the blindingly obvious (don’t knock it). We live life through physical space and time. Growth, decay, dissipation are incorporated into human cultures. In a more useful sense, successive historical periods make use of and value specific qualities of physics over others. Some of these obdurate qualities lead to predicted inefficiencies such as the practical entanglement of bitcoin which clogs up the whole system. Bitcoin is essentially inefficient because the system both seeks out more powerful computers to run faster calculations, but also is threatened by them. Vastly more powerful quantum computing could present a threat to the blockchain’s security, though it is by no means a closed deal. It is not a story of linear progress but comes about due to a specific entanglement of community and mathematics that makes bitcoin possible in this time and place.

Maxwell D, Speed C and Pschetz L (2017) Story blocks: Reimagining narrative through the blockchain. Convergence: The International Journal of Research into New Media Technologies 23(1): 79–97. DOI: 10.1177/1354856516675263.



Software for the gravid researcher

Everyone develops their own set of software tools over time. There are lots of handy specialist software programmes out there. Look out for those that have a strong support community around them. This is what makes R such a pleasure to learn and Nvivo so frustrating. I’ve selected ones I use that should be useful for researchers whatever their specialism.

For my references: Zotero with WebDAV for file syncing. A free pCloud account gives me 10GB of storage. I will never need that many pdfs. It works with Google Docs which is another bonus.

For presenting: Google Slides – shareable and simple.  The requirement to use Powerpoint for digital lecturing has put the kibosh on this for the now. Portability matters a lot to me and being able to drop work on the laptop and immediately pick it up on an iPad smooths things out.

For literature reviews: any spreadsheet programme will do here. I use Numbers because it’s free, on my mac and iOS, and the file saving system means it writes changes immediately – no crash risk. I started using a spreadsheet for literature reviews following a post by Elaine Gregerson and see also

For extended writing: Scrivener is one of a few word processors that are designed with writers in mine. Draft, store, organise and restructure anything from an article to a book.

For collaborative writing: Google Docs is easily shared and has a low barrier to entry. I’ve long since surrendered to the vampire squid on that front.

There’s plenty of other incidental software and platform choices we make – email, word processing, cloud storage, anti-virus and it’s worth taking the time to use ones that will work for you.  Here’s one overview to selecting the right tools and developing the right cast of mind:

Is it a university building or an unethical experiment in social psychology? Why not both?

Visiting the art college and the Informatics school reminds me how uncreative most academic buildings and arrangements are. Many bear a striking resemblance to Dunder Mifflin, a monastery made of MDF, or the offices of a well established but slightly shady law company specialising in lengthy divorce battles. Students are often skilled in working the space to find places to work and gather. Talking to them about how they use the space is a revelation.

Looking closer, university estates provide an archeology of educational theory and a record of the changed purpose of the university in society. This is why some of the buildings appear to be designed by a rogue group of social psychologists testing the effect of de-spatialisation on the human mind. Floors that are undifferentiated expect by a small patch of differently faded carper. Naming conventions produced by a random number generator. Several university buildings I have visited decided to letter rather than number their floors. Even better, one chose not to letter them alphabetically. There appeared to be a missing floor. These strange choices are often the result of elements of building design that no longer apply to its current use. Floors were named after departments or teams that no longer inhabit them. Two separate buildings were joined at different levels and in an unsuccessful attempt to make things less confusing two different numbering systems were used.

Myths emerge around the building, of secret passageways and hidden rooms. Some of these are true. One building had a set of rooms that had been forgotten about for decades and were uncovered when someone shifted a filing cabinet from its 30 year perch. In another students had gained the impression that they were not permitted to go about the first floor. This was partly based on reality. The building had been designed to encourage them to remain in the lower floors where most student services were located. It tells of who the building is for. Wouldn’t it be better to design it so that students and staff mingle? It’s preferable to monastic silence and self isolation. Are we a community of scholars or an education industry?

Many more aspects of design and location tell you about the relationship between the university and society. Is it on a campus or distributed? What are the buildings made of? Which departments would you expect to find in stone buildings compared to brick or concrete ones? Where is the administrative centre, relative to the literal centre? What is the public face? These aspects tell us what the past, present and future direction are. Sometimes the news is good, sometimes not. There are many who would abolish the university as a site altogether and have us study and work in no-place. Even no-place matters though, and universities remain sites apart for a reason. Sometimes they are places of violent struggle and resistance, for a reason. They should not be tame.


Stabilising security market abstractions

My study is illicit market ecosystems. The counterparts are global insecurity markets. These are growing markets in policing and public order solutions. They extend from gear – riot shields, body cameras, conducted energy devices like the Taser – to eyes – surveillance methods, smart policing algorithms. Each device produces a receiving context in which it makes sense to use it, in which it is rationalised. Tasers provide a way of using force with restricted lethality, but also lower the threshold at which force might be used and open its use to a wider range of actors beyond qualified law enforcement officers. Hence overall there may be more incidents of injury as a result. They also change our understanding of what force is in relation to policing. They do not do this on their own, as each technology is introduced in the context of regulatory and customary controls around its use.

Increasingly products are offered as part of complete solution packages. Optimised police patrol routes, evidence production, and connected technologies are offered as a single product. Principles of risk reduction and redistribution are embedded in them. That may be reducing the space for customary regulation and make police much more a matter of bureaucratic governance through these informationalised protocols. Systems create abstractions which are then reimposed on the local context. They build in ideas of optimisation, efficiency and effectiveness which are produced in specific contexts but have the experience of abstraction.

Markets cover two entities: knowing objects and stabilising objects. A knowing object could be an algorithm to decide an optimal police patrol route in a city. A stabilising object could be a shared evidential collection and chain-of-evidence system.

The knowing object is novel as it does not require a human in the loop, unlike Bentham’s panopticon. The interaction between individual and object has evolved. Under the panopticon, the threat to the inmate is of not knowing when you are being surveilled. The direction of surveillance and behaviour change is from centre to periphery. The knowing object changes the equation. It surveils both parties – the warden and the inmate, the police officer and the suspect. It changes how both act towards it. Both have to respond to and make use of its knowing, information processing qualities. There is a challenge there for established Foucault-influenced theory of modernity, self and surveillance.

The stabilising object is directly connected to questions of what is and what matters. It creates coherent, shareable concepts of what evidence is, what a particular type of crime is, where it can be found and who has liability. So far in those that I have looked at they tend to be more public and less commercialised. They are often shared between law enforcement, policy makers, researchers and private entities – so they are less products of the market and more like mediating factors. A case is the Budapest Convention on Cybercrime and the related Octopus platform for sharing evidence and intelligence. These objects are active in shaping what we understand cybercrime as being. Cybercrime is itself a diffuse concept and is built on shaky ground. The ‘cyber’ part seems both unspecified and potentially all encompassing. What criminal doesn’t use some aspect of ‘the cyber’ at some point? The objects govern that down to a manageable, tangible construct.

Oriola T, Neverson N and Adeyanju CT (2012) ‘They should have just taken a gun and shot my son’: Taser deployment and the downtrodden in Canada. Social Identities 18(1). Taylor & Francis: 65–83.
White MD and Ready J (2009) Examining fatal and nonfatal incidents involving the TASER. Criminology & Public Policy 8(4): 865–891. DOI: 10.1111/j.1745-9133.2009.00600.x.
White MD and Saunders J (2010) Race, Bias, and Police Use of the TASER. Race, ethnicity, and policing: New and essential readings: 382–404.

Reduce the problem space

In the early stages of the PhD, you work to ‘reduce the problem space’. Define the problem using the smallest number of steps, variables or data points. This shows you the scope of your study. Then elaborate the problem in as many contexts as you think it applies. This shows you the applicability of it. Most of our advice boils down to ways of doing both. The term comes from computer science. The initial problem state and solutions to it are partly unknown. The algorithm is designed to propose potential solution states which are selected according to a heuristic.

All research involves a heuristic: a way of engaging with the research material or context that captures some essence about it. Sela-Smith (2002) characterises it as tied to phenomenological research where the research seeks discovery by recreating the research subject’s experience. My view – one shared with her – is that a heuristic approach is involved throughout any kind of research or theory. In qualitative research it is more usually tied to the self, and therefore becomes more apparent. However it is quite typical in other methods too. A theorist uses a heuristic reading of his or her subject matter. A computer scientist using human in the loop machine learning is doing the same. As Sela-Smith says, heuristic scholars can be found everywhere research is being done. They have some notable qualities, not afraid to take risks, or recommit to learning throughout their lives. Much of this is quiet though, taking place often in places that are not on the list of high status academic achievements.

Sela-Smith outlines six aspects of the heuristic approach: ‘indwelling, tacit knowing, intuition, self-dialogue, focusing, and the internal frame of reference’. I word this as consistent self reflection, developing a feel for things/getting skin in the game, sparking understanding, creating the internal conversation, living with the problem and knowing the problem set. Framing it as I have pulls it away from Sela-Smith’s focus on the self so it changes the meaning and quality of the heuristic, so you should look her article to understand it. Each element has a related activity: ‘initial engagement, immersion, incubation, illumination, explication, and creative synthesis.’ Some of these tasks will be very familiar to the PhD researcher.  Each can be broken down into sub-tasks which contribute to the whole. For example, developing a taxonomy can be part of incubation. Creative synthesis is an activity that I have seen some PhDs do very successfully. They use methods such as creating fictions from the data in the form of narratives of what was, is and might be.

The problem space approach might seem to work against creativity as it focuses on producing defined solution states. However if we see the solution state as being a product of the creative process then it fits better. One different I have with the heuristic approach is that it tends to focus on the individual, rather than the researcher as embedded within a community. Researchers might feel the individual qualities of the heuristic process as being demanding or involving risks e.g of self-alienation. I can see risks in piling requirements on the emotional labour of the PhD. This is where having a creative community comes in, where the work is held within the group rather than being a lonely labour of love. Computer science has a set of explicit protocols to do this but social science does not so that is something we can learn about.

Sela-Smith S (2002) Heuristic Research: A Review and Critique of Moustakas’s Method. Journal of Humanistic Psychology 42(3). SAGE Publications Inc: 53–88. DOI: 10.1177/0022167802423004.


How to decide what articles to read

Is someone likely to use it to detonate under me at a conference (‘but surely you know that Bodkins has covered your very argument in her seminal work in the journal of Ipretendtoreadit studies?’)

Am I likely to meet the author and give them a bland ‘loved the article’, and are they the type of person to say, ‘what did you like about it exactly?’

Is it too recent to be written off as outdated, and not recent enough to be ‘provisional findings I can forget about’?

Can I write a contrarian reading that will at last establish me as the unparalleled lone wolf polymath I know myself to be?

Will I look like an idiot if I haven’t read it?

Will I look like a wierdo if I have read it?

Can I hate read it?

Does it finally define ‘habitus’?

Can I crib their bibliography?

My reading list for the next wee while

Inspired by a discussion of corporate fraud and wage theft I am aiming to incorporate this work into my Illicit Markets course:
Smith D (2013) The “Hyper-meritocracy” – an Oxymoron Led by Criminal Morons. In: New Economic Perspectives. Available at: 14 October 2020).
Akerlof GA, Romer PM, Hall RE, et al. (1993) Looting: the economic underworld of bankruptcy for profit. Brookings Papers on Economic Activity; Washington (2). Washington, United States, Washington: The Brookings Institution: 1. DOI:
A discussion of race and cybercrime is much needed for me and the insight, critique, creative energy and intellectual generosity of Martin Glynn has helped push me in this direction. He is a criminologist, dramatist, children’s author and creative storyteller.
Glynn M (2019) Speaking Data and Telling Stories: Data Verbalization for Researchers. Routledge.
Following a discussion with Alistair Fraser and Oana Petcu, I am reading work the digital street:
Ilan J (2020) Digital Street Culture Decoded: Why criminalizing drill music is Street Illiterate and Counterproductive. The British Journal of Criminology 60(4): 994–1013. DOI: 10.1093/bjc/azz086.
Lane J (2016) The Digital Street: An Ethnographic Study of Networked Street Life in Harlem. American Behavioral Scientist 60(1). SAGE Publications Inc: 43–58. DOI: 10.1177/0002764215601711.
Lane J (2018) The Digital Street. Oxford University Press. Available at: (accessed 27 May 2020).
Colleagues in Sweden, Denmark and Canada nail down a long standing debate about drug market prices between street and screen:
Moeller K, Munksgaard R and Demant J (2020) Illicit drug prices and quantity discounts: A comparison between a cryptomarket, social media, and police data. International Journal of Drug Policy: 102969. DOI: 10.1016/j.drugpo.2020.102969.
A deep delve into digital materialities is needed and I’m indebted to Elif Doyuran for this reference:
Dourish P (2017) The Stuff of Bits: An Essay on the Materialities of Information. Cambridge, UNITED STATES: MIT Press. Available at: (accessed 9 October 2020).
leading to the following theorisation of AI and algorithms:
Hayward KJ and Maas MM (2020) Artificial intelligence and crime: A primer for criminologists. Crime, Media, Culture. SAGE Publications: 1741659020917434. DOI: 10.1177/1741659020917434.
Christin A (2020) The ethnographer and the algorithm: beyond the black box. Theory and Society. DOI: 10.1007/s11186-020-09411-3.
Ian Walmsley kindly got in touch to share his work on the critical history of withdrawal in addiction research and medical thinking – this has been exceptionally helpful in crystallising my thinking:
Walmsley I (2012) Governing the injecting drug user: Beyond needle fixation. History of the Human Sciences 25(4). SAGE Publications Ltd: 90–107. DOI: 10.1177/0952695112459135.
A lot of re-reading has been done this month as I return again to the work of Fay Dennis and Cameron Duff on drug materialities.

Is it problematic, or just annoying?

Is it neoliberal, or just capitalist?

Is it performative, or just displayed?

Is it paradoxical, or just contradictory?

Is it dichotomous, or just separate?

Is it criminalised, or just unlawful?

Is it totemic, or just symbolic?

Is it a practice, or just an activity?

Is it bounded, or just limited?

Is it a social construct, or just a creation?

Is it a moral panic, or just an upset?

Is it a structure, or just a pattern?

Do I fixate on the overuse of neoliberalism too much? Almost certainly.