Scripturient Interview: Karen Levy, Road Scholar

In the latest issue of Information Professional, my column ‘Scripturient’ features an interview with Cornell University’s Karen Levy. An assistant professor in the Department of Information Science, and associate member of the faculty of Cornell Law School, Karen researches how law and technology interact to regulate social life, with particular focus on social and organizational aspects of surveillance. We spoke as her new book Data Driven: Truckers, Technology, and the New Workplace Surveillance was being released in the UK. You can download a PDF of my Scripturient column featuring Karen here, and a transcript of our conversation is below.

An NPR radio broadcast inspired you to explore trucking and surveillance as a grad student. As researchers we hear about so many potentially intriguing topics every day; why did this one grab you?

It’s funny to look back on these moments and ask, “Which of these trivial interactions ended up being extremely significant in your life?”

Around 2011, when I began to research trucking, people were starting to get interested in ridesharing and platform-based labour. Also, I think the road is where everyday people, not just truckers, often interact with rules and technology in this very acute way. So I was already thinking about traffic, and the road as a potential research site, when I heard this story, and, well — have you ever spent time around truck drivers?

Sadly, no.

Well, I’d put that on the bucket list! They’re just really easy to talk to. For people in our line of work, sometimes it’s difficult to get access to a field site — but this one was really easy and fun, so I just stuck with it.

Tell me a bit more about that first encounter. You went to a truck stop in Portland, Oregon, right?

I can’t quite remember if it was that very day, but it was certainly that week. It was very soon and very casual, just to see, “Let’s see how this goes.” I was in graduate school and in a mode, which I still try to impart to my students, of being a sponge as a really useful way to start a research project and to keep the spark of research alive for yourself. Just go out into the world and see what’s going on in ways you maybe hadn’t been exposed to before. I always try to be open to these kinds of things.

And truck stops are really interesting places! Many of us pass through them, we use the bathroom or get gas and then we move on, but we don’t talk to anyone. But there are whole books to be written about truck stop culture. 

How did you get started?

One of the early things I did was to find an ally there. A lot of my first interviews were in the bars at truck stops, because that’s where truckers go when they’re on a break. Time is so important to them, I didn’t want them to feel they’d be losing money by sitting to let me interview them. 

So early on, I made friends with one of the truck stop bartenders, one of the only other women there, who knew a lot of the regulars and would point me to people I should talk to. It’s always helpful to have a local confederate who can help you navigate a new setting. And truck stop bartenders have really seen a lot!

What preconceptions did you have at the outset, and what changed for you as a result of this research?

I’m from a red state, Indiana, and though I didn’t know a lot of truckers personally, I know people who are like truck drivers – this wasn’t super unfamiliar territory to me. 

Some of the misconceptions I had were connected to trucking’s very cowboyish, masculine identity, which is espoused and embraced by a lot of truckers in a way that can be funny and a lot of fun. You hear it in the songs, for example. And it’s okay to have fun in your research, so I think it was okay to enjoy that.

But I also think that I hadn’t fully recognised what it was a response to. One interview I did, a driver really set me straight on this: it may seem really fun, but we only have this attitude because this work is frightening as hell. This work is scary stuff that we’re compelled to do because otherwise we don’t keep the lights on at home. That was really useful to me as a researcher, to recognise that there’s a lot going on simultaneously, the culture is part of it, but you have to recognise what the culture is doing for people.

You use that term “self-sweating”, for when people are complicit in their own exploitation. 

In fact that makes me wonder, especially with all the talk of artificial intelligence being used in higher education, the precarity of early career researchers, if there are parallels between trucking and scholarly careers right now!

Anyone who’s paying attention recognises that workplace surveillance and productivity tracking, the measurement of work in different ways, are not new phenomena, but they are also becoming more pervasive – including in professions which were previously immune from those dynamics.

Academic life is one of those professions where there may be more tracking and more forced quantification of labour, though I am still very lucky in having a lot of autonomy to do the work I do. By no means would I say that what a trucker does and what I do are exactly the same; there are marked differences!

But there is something to it: a lot of people get into academic life because they desire this autonomy, which is a big feature of the job, and the same is true for trucking. Autonomy may mean different things, but with the casualisation of academic labour and the adjunctification of the academy, you see some of the same dynamics which appear in trucking and lots of other professions. It’s maybe just a fact of modern life.

One of the most exciting things about the book is that it’s such a great case study: watching the most independent working culture, being subject to the most stark surveillance, is like seeing an early tide come ashore which might also be heading for other parts of our society.

What have been the ripples of this particular project for you, shaping what you’ve gone on to research and how you go about that research?

It’s led me to reflect a lot on why we study technology. Many people are devoted to the study of digital technology and information, but the argument that I and others would make is that the technology is kind of a red herring and the real thing is capitalism, or industrial organization, or exploitation. The technology is important, but it’s a part not the whole. So why have we all decided to study technology?

I think about this a lot in the policy sphere. I’ve had some opportunities to think about what lawmakers or policymakers should be doing differently, and oftentimes I think it puts you in a funny spot: technology policy is a limited way of getting at the economic reform, or political reform, where you might feel the ball should be.

Where is your research taking you now?

A lot of what motivated the work in Data Driven is this inquiry into rules, how rules function, and how rules interact with technologies meant to enforce them more consistently or more perfectly. 

That’s where I started the project, and how I selected the field site, but I don’t think I answered all my own questions in the one book. The book gets at how that works in one particular context, but there’s more I want to do. My next project is going to be about the idea of loopholes: how it is that one thinks about rule-following, when we actually mean the rules we say we mean, and how do people navigate that. 

I think technology is an interesting strategic site for examining that, because it makes us confront whether we really mean our rules or not.

What is it about rules that interests you, why are you drawn to this topic?

I guess I was a rule follower as a kid, and only later did I think, what are these rules anyway? Do we actually mean them when we articulate them?

I went to law school after undergrad, not because I really wanted to be a lawyer, but because that interest in rules was already there. I was very interested in what rules are doing for us. How are they organizing society, or shifting power around to different people? Sometimes the real effect of a rule is not apparent on its face, in the same way that a digital platform functions: you have to really dig into the way it’s used to understand how it’s working. There’s an interesting parallel there between rules and digital technologies.

However, if you go to law school, nobody’s going to hire you to wax poetic about what rules are! They expect you to actually work with those rules. So that’s why I went to get my PhD; I had much more interest in the sociology of the system than working within the system.

That idea of waxing poetic is interesting. Finding a space to talk around the law, rather than only within it – creating a bigger context to talk about things.

Yes — recognising when rules are serving the function of communicating something to people, and sometimes what’s being communicated is not what we intend. There’s a concept in legal studies called acoustic separation, which suggests that sometimes what a rule is saying, like “Don’t kill people under duress” or whatever it is, is not the same as articulating the consequences which will befall you if you do that thing. 

Rules seem simple, but they are really complicated. Lorraine Daston’s new book on rules is great on that. You’re thinking about what the language is doing, what the social institutions that enforce that are doing, and the paradox of how we sometimes think it’s really unfair if rules are followed to the letter. All that stuff just keeps me up at night, I really like that stuff!

That question of language interests me too. In the course of your research, you’re acquiring trucker slang, drawing on country music lyrics, and incorporating legal and social science terminology. What was your experience of learning and translating between languages and registers in your writing and research?

I have a CB Slang dictionary on my shelves, because at first I had no idea what people were saying to me! I’d have to keep stopping them to ask, “What does that mean?”, but sometimes you really want to let people just go on and you don’t want to stop them all that much. It took some time to pick up the argot of trucking, which is a pretty common experience in ethnographic research, getting used to the local language. Sometimes it’s literally, “What does this word mean?” and other times it’s, “What are people referring to?”

Throughout the project, I talked a lot with truckers in-person, but also with trucking media. I’ve been on trucking radio shows, talked to trucking publications; there’s a whole trucking and logistics media ecosystem which has been great fun to engage with. When you talk to a sociological audience or a legal studies audience, explaining how trucking works gets you through the first twenty minutes. When you talk to truckers, they know all this stuff; you don’t have to translate or explain for them. 

That experience was really useful for me, because I had to recognise what I had to contribute to an audience on trucking media, compared to an audience at a law school or another institution. There’s a different aspect of the story that’s unknown to each of them.

That’s probably true for many scholars, that you’re in the middle of at least two circles on a Venn diagram and maybe more. Figuring out how you serve as an ambassador or do translational work from one to the other is hard, but it’s also a really fun aspect of our work that I think we could talk about even more.

What did you feel your perspective brought to the discussions on trucking media?

Any time you can say the stories that you are telling about your life are being heard by someone on the outside, who, for better or worse, has ‘ins’ with different communities than you have, there’s a hope that that will get you somewhere. 

That can be legitimating, or can help bring attention to the issues they face. It’s not necessarily contributing knowledge that they don’t have, but it’s a recognition that their life experiences are being heard. That’s a lot of what truckers want: if you talk to a truck driver, or read their comments on pending regulation, a lot of it is just asking that they be listened to. They want you to know what the problems are, not what you think they are, not what the regulators who have never been behind the wheel think is going on. “Just hear us”: that goes a long way on its own.

There’s an ethical aspect to that, too: you refused to be a “tattle tale” and give presentations to tech manufacturers on the workarounds that truckers were using on their surveillance devices.

How did you reckon with your power, privilege, and position in this research? 

It’s something I think about a lot, without necessarily coming to a fixed conclusion about how to wield one’s own power in an ethnographic pursuit. As many researchers will say, the starting point is being reflexive about the way in which one’s own identity gets you things, or shapes what it is that you are able to access, or how you are received by different groups of people. “How can I do this in ways that other people wouldn’t?”

You write about this very nicely in an appendix to Data Driven: how being a white woman scholar opened some doors and encouraged truckers to feel they could explain things to you, while also presenting new challenges such as requests that you be chaperoned when visiting a loading yard.

As you say, I was a fairly young white woman from Princeton when I started this research. That opens a lot of doors which aren’t open to everybody, but it also closes some doors. I was pregnant when I was doing some of the work, which was an impediment in some ways, but also opened the door to other informants. Just being aware of that is hugely important, though it is not the end point.

In this project, and more generally, thinking about who it is you want to be communicating to is key. I really value public writing, as you do too; I think writing for the public, though it may not be something the academy necessarily rewards, is essential. If we feel the work we’re doing is worthwhile, I feel a personal responsibility to disseminate that to people who might be able to change things. That might mean involvement in policy, or writing in public venues, or talking in my case to the trucking media. 

It might also mean refusing to do other things; as you mention, trucking vendors were keen for me to tell them how people were cheating their devices, but that didn’t feel like a legitimate or respectful use of my informants.

There were so many different interest groups within the trucking ecosystem at the time of your research. When you look back on electronic logging devices coming in as a mandate, who ultimately benefited from that?

Clearly the manufacturers of the devices themselves; also trucking firms, as the devices become the scaffold on which you can hang all other kinds of workplace surveillance. 

Larger firms especially benefited; they could afford the devices more, and could reap the benefits of analytics more. They initially opposed the mandate but turned towards approval as they recognised these side benefits. Third-party data companies, too; people who sell parking spot reservations to truckers, insurance companies, there are a lot of people who really like having this data. 

It’s not hard to find such beneficiaries, but those who are not benefiting are the truckers themselves and the general public, because the safety benefits which were being touted before the mandate have not actually been realised. This was supposed to give us safer highways, but that doesn’t seem to have been an outcome of this policy change.

I was shocked to learn about the parking reservations – that truckers were paying $15 out of their own pockets, unreimbursed, to reserve somewhere to park at a truck stop!

I guess that’s an example of the power of simply recognizing and retelling what is going on, as you mentioned earlier.

Regarding the safety benefits, it’s my understanding that some of the rules were relaxed during the pandemic, and it didn’t lead to an increase in accidents, is that right?

During the pandemic, as has happened during other crises, the government temporarily lifted some timekeeping regulations that truckers are subject to, because it’s important that goods get moved. 

This was of course an odd time in many ways, and there weren’t as many cars on the road, but it is interesting that there weren’t any more accidents, and people seemed quite able to conduct themselves safely without the surveillance mandate in place. It becomes almost like an existence proof that hyper-rigid rules are maybe not all that useful or necessary.

The other thing that started to happen was some recognition in Washington D.C., for the first time since I started this research, of some of the real causes of danger and fatigue on the roads. Bills have been introduced in Congress around paying truckers overtime and removing their exemption from fair labor laws, guaranteeing them bathroom breaks, and other measures which recognise the dignity of the job and address the problems of the job.

I don’t know what will happen, I think we’re still a ways from meaningful reform, but I also think we’re closer to it than we have been since I began researching this industry. That’s been exciting to watch.

I’m minded of Mimi Onuoha, whose work you cite. Writing about whether evidence of racism is enough to effect change, she says: “Love, justice, data — alone, none have been enough.”

How does Data Driven contribute to that conversation around what is necessary for change to happen?

I’m so pleased that you bring up Mimi’s work – I teach that piece, and think it brings such an important perspective. There’s an inclination among a lot of social scientists – which I still subscribe to, to some extent – to say “I’m just going to document the thing, tell people what’s happening, and that’ll do it.”

Sometimes that does happen. Sometimes a powerful piece of writing, or a legal brief, or a story in the New York Times, is actually enough to catalyse public opinion or change policy. We keep that in mind alongside Mimi’s very important recognition that we can’t just rely on that, or assume that will always happen. What’s known about injustice has been known for a long time; I’m far from the first person to express them in the context of trucking, for example. It’s not that we don’t know what’s going on. Yet in sociology we can find ourselves simply finding new ways of documenting the same problem, so it’s clearly the case that having documented them before has not changed anything.

I wish I knew what does change things for sure. It’s a heterodox situation and there are many ways of doing stuff. We must find ways to elevate the voices of people who’ve been affected by something. People say those words a lot, it can sound trite, but ethnographic research is well-placed to do this; it’s the bread and butter of good ethnographic research.

Finding ways to report not just on the numbers of accidents, or the number of hours somebody works, but to recognise and legitimate the human experience of those things? That can be really powerful. In my classes we talk, as many scholars have, about stories and statistics. You don’t have to do both, but the combination of registers can be very powerful. For some things, quantitative data is really useful in effecting real change; other times, it might be someone’s story, like a woman who had to get up really early in the morning to go to a shift at work. 

In addition to that combination, it’s about being at the ready: when it comes to public writing, it’s about being prepared to enter the wider conversation when it’s ready for you. It’s not always the moment to argue for a particular type of change, there may not be receptivity to that. But keeping at it, and picking your moments, may be the best we can do.

That balance seems important to me. Rafael Ramírez at Oxford jokes about quantitative “eau de credibilité”: spritzing numbers onto a discussion to make it seem more substantial than it is.

There’s a lot of quantification in the age of digitally surveilled trucking, and there’s a lot in your book about how law enforcement officers, too, struggled to make sense of some of the outputs and displays of the new devices which replaced the traditional logbook. I wondered: to what extent did you have to get acquainted with the tech and the data outputs of these devices?

I didn’t have to do my own quantitative research on things like safety impacts and the numbers of accidents; others had done that work, which I was able to draw on. Not every researcher has to do all the things, they just need to be able to draw on different methodologies and recognise what those methodologies are and aren’t able to tell you. 

I did try to become as familiar as possible with the actual technical detail of the devices, which meant reading a whole lot of product manuals. It wasn’t fun, but it was actually very telling. It was a form of research I certainly hadn’t done before.

I attended regulatory meetings where there were very specific discussions about the kind of standards people wanted to attach to these devices. Some of it is very technical, but over time you come to understand more, and to understand why these issues are important. 

Regulations change, and it’s even the case that many of the standards I was looking at are moot now, so the goal is not just to understand the thing as it is, but to understand what it is representing to people, and what the debate about standards means for how people are going to represent their interests, or how they are going to interact in the field.

In the book, you talk about this idea of what is being represented: how rumours still usefully tell us something about how a community is grappling with an issue, and how, even when an informant is performing for you as the researcher, exaggerating or telling tall tales, that is still useful.

Yes! Jenna Burrell has this great piece on rumours; Jill Fisher and Torin Monahan have a great piece on observer effects – how people tell the story to you and what that means for how they want to be perceived. I teach these pieces, because I think it’s important for students to recognise how much we learn based on what people do and don’t say. We might not get to see people doing the thing, but it can be interesting to explore what they want you to know about what they’re doing, or what they want you to think about who they are. Depending on the question you’re trying to answer, that might be more interesting data anyway.

There’s something there about what you might need to unlearn, as well as what you might learn…

Earlier, talking about injustice, you said, “It’s not that we don’t know what’s going on.” That makes me think of the things we don’t yet know: there’s a lot in the book about the future of trucking, particularly how it may be automated. You mention the “Jetsons fallacy”, whereby people think that the tech will change but society and culture won’t.

So much of this work is about visions of what is going to happen, and how those visions affect decisions being made today? What did you find in your research about the role that the future plays in these discussions, and has the research shaped your own thinking about the future of transportation and logistics?

People love to talk about “the future of work”. I don’t know why that has become such a stock phrase compared to, say, the future of education or the future of criminal justice. I resist that, because it’s like othering: you over-exoticise something by locating it in the future, and become more prone to committing this Jetsons fallacy where you fail to recognise what the social and cultural conditions of the moment are and how those will read onto the technology, but you also fail to recognise the through-lines between what’s happened in the past, what’s happening now, and what might happen in the future. Those are really important indicators, too, and sometimes I think it’s a mistake to exoticise what’s going on. 

Generally, if you go to the people who are actually doing the thing, you get a different sense of the time horizon. I went to a meeting of people who work in trucking companies and make decisions about investments in tech, and they were talking about self-driving vehicles in terms of forty years in the future, not at all the time horizon that you hear in the media, or that start-ups want to promote, as if autonomous trucks are going to be on the road by 2026. You hear more about what the conditions of the present are and how they are going to affect things going forward. 

In this context, for example, there’s no real reason to deploy autonomous technology if it’s not cost-effective, and so long as it still requires a human to be in the cab, you’re still going to have to pay that person. 

Keeping the conversation firmly rooted in the present, and recognising the path dependency of some of these things, is probably a good starting point before we go dreaming about what work in 2050 will look like.

I think of Donna Haraway’s phrase, “staying with the trouble”. 

It made me wonder about a bit in the book where you talk about “dirty work”, and the pride that can be taken in such work. I saw this riding with self-driving combine harvesters in Australia: the remaining tasks of the driver were the dirtiest and most dangerous ones, cleaning spindles and avoiding flammable build-ups of cotton. The man is almost in service of the machine.

In trucking, does technology make dirty work dirtier?

A lot of what allows truckers and others who do so-called “dirty work” to do it with pride, is autonomy. Trucking is hard, requires a lot of stamina, keeps you away from your family, and is not high quality work in many ways – but its great redemption is that you get to decide how you do it. You pick your route, decide what time to leave, make decisions about what to do when there’s a storm. 

I haven’t studied farming in the same way, but perhaps there’s a similar sense that some of the autonomy gets removed when you’re a “machine minder” now, and you are really just troubleshooting. In trucking, more and more of the work is done in tandem: either they are supervising the machine, or the machine is supervising them. This reduces their autonomy to make decisions, or opens up their decision making to the scrutiny of a dispatcher or someone who may want to overrule their decisions based on additional data to which the driver isn’t privy.

I’m thinking about the Australian context for that combine harvester, and the US context for your work, and the figure of the cowboy that comes up in trucking: is there something specifically North American about this? Have you looked at trucking in other contexts? Is Data Driven an American story?

The fieldwork was confined to the US, partly because I’m based here, and partly because the story I wanted to tell is shaped by the very specific regulatory history and political economy of how trucking has developed in the US. 

I have had conversations with other people who have experience from other countries and there are sometimes similarities; with Australia, as you mentioned, where there is a big landmass and there are long, fatiguing distances to be traversed, there are similarities. In Canada and Mexico, there’s interesting stuff going on with cross-border trucking and how the policies in each have come to mirror the United States, with how much trust there is in Mexican drivers versus Canadian drivers, and in fact most of the Canadian provinces are now rolling out an ELD mandate similar to that in the US. In the UK and European Union, it’s somewhat different as they use a different device, known as a digital tachograph, and I know less about the culture and the way it has evolved there. Trucking in the global South is different again, especially as the political economy is different and you don’t have the dominance of a few giant firms as you do in the US, and that changes aspects such as the way the labour force is organized.

What does this journey you’ve been on with American long-haul trucking tell us about good regulation and governance?

If you’re going to regulate something, you should regulate the actual problem. Often regulation is not aimed at solving a problem but avoiding solving a problem. As I write towards the end of the book, in trucking, the ELD mandate and 25 years of debate over whether we should have electronic logging, is almost a sideshow to avoid solving the actual problem, which is: how do we pay people for the work that they do and ensure that they don’t, sometimes quite literally, drive themselves to death because they can’t keep the lights on at home?

Because we’ve been unwilling to solve the harder problems about economy, politics, and industrial organization, we’re left with technology as a kind of duct tape on the system. 

This happens all over the place: it’s not just a transportation thing, it’s not just a trucking thing. If you look at criminal justice, education, any place where there’s a resource limitation, we turn to technology and say: “How about we do this to stretch what we’ve got here?” 

Oftentimes the tech is a clue that you’re not solving the real problem. You’re not creating enough housing for people, or you’re not actually allowing people the opportunity to get an education, or whatever it is. The tech is a sideshow solution, a way to do the thing without doing the thing.

Thoughtful regulation, including technologically oriented regulation, can be quite useful. There’s been interesting activity, globally and in Washington particularly, about AI and the guarantees we give people about how they interact with automated systems, but I also think that we can’t let that overshadow substantive economic reform, labour reform, or other reforms which, if we saw those shifts, wouldn’t necessitate the shift to technological solutions.

A couple of final questions: one is about sleep as a battleground. Fatigue is so fascinating in trucking, the thought of new ways of policing that or intruding on that domain is challenging and intriguing.

I was on a truck radio show recently and a trucker called in who suffered from sleep apnea, which is very common among truckers. He has a sleep apnea machine, and his company gets access to the data: if he gets less than seven hours, they call him up and ask what’s wrong. Sleep is such an intimate activity, it has something to do with productivity but it also has to do with human flourishing, and when we operationalise it as fuel for the efficiency machine, it’s a very interesting way of looking at this fundamental function. 

The historian Alan Derickson’s book Dangerously Sleepy is great: it talks about how, across this great variety of disparate professions from stockbrokers to medical residents and beyond, sleep is a battleground. Kate Kellogg, too, has an excellent book on sleep and fatigue among medical residents. It’s interesting to think of sleep as a tool in the managerial toolkit to ensure that workers are up to the task, where again, it falls prey to the issue of whether you’re solving the right problem. Oftentimes rather than changing the incentives or core conditions which lead people to overwork, we just say “Also, make sure you sleep eight hours!” We turn into yet another thing that we’re tracking, when maybe we should be changing the way people are paid, or the conditions of the work, so that they can legitimately get sleep on their own terms.

You use this great term sousveillance, watching the watchers as a form of resistance. How does that come up within long-haul trucking? Are there opportunities to “watch back” at these surveillant systems?

If you think of this, not as watching the party that’s watching you, but watching up or watching those in power, then an interesting way in which it’s happening in North American long-haul trucking is using all of this data about what truckers are doing to learn about what shippers and receivers are doing. Shippers and receivers have a lot more power in the logistics landscape, and they exploit the power through things like “detention time”, making truckers wait unpaid hours to unload. Shippers and receivers have very little incentive to change those practices, but this is where a lot of meaningful reform could happen.

Because we now have all this data on what the truckers are doing, a side effect is that we have data on which shippers and receivers are the worst actors. You can see which ones cost you the most detention time. It doesn’t redeem the entire project of the ELD mandate, but it does create a nice silver lining, because arguably that data can be useful to truckers as they make a decision about whether to accept a load, or how to negotiate terms with that entity.

Finally, you have an interdisciplinary position between information science, sociology, and the law: what are the tensions and benefits of sitting at that particular juncture?

The disciplines differ in the degree to which the goal of the work is seen as being to describe the world in a quasi-neutral way — allowing for the fact that nothing is really neutral. In some disciplines, it’s enough to say “here’s what I found!” and not make a policy recommendation or a design recommendation, or take a position normatively; whereas in other disciplines, such as when writing for a law review, there’s much more expectation that you conclude by answering the question, “So what should we do?”

I always struggle to figure out which one of those I am: am I the person who has a three or four-point plan for how we fix these things, or is it enough sometimes just to say “here’s what’s happening the social world”, which gets into the conversation we had earlier about Mimi Onuoha’s work. I think it’s legitimate for researchers to stake out different positions on that spectrum.

I find that it’s sometimes an interesting tension to navigate, especially in terms of writing for different audiences. The nice thing about writing a book like Data Driven is that it can reach all audiences, in a way unlike conference papers or journal articles which are oriented towards a particular set of peer reviewers or readers. The book helped me in many ways to navigate that tension, though I have to say for me any tension is counterbalanced by how much fun the work is. With information science particularly, it’s a very catholic discipline and there’s a whole lot of stuff that you can do. The knowledge products can look very different; colleagues in my department do all kinds of things that don’t even look like the same style of work; I really love that, I learn so much from that cross-pollination. 

What message would you want information professionals to take away from Data Driven?

My hope is that, though the book is about truck driving, that’s a case study of a broader range of dynamics which information professionals deal with all the time – especially around “future of work” and related issues like productivity monitoring. 

I think it’s useful to think about the ways in which information collection systems do and don’t capture aspects of the work people do, or the ways they live their lives, and then how organizations and human relationships shift around those data collection regimes. 

Information professionals will be intimately familiar with those questions, but I hope that the way in which that’s manifested in trucking is of interest!

Find out more about Karen Levy and her work at her website,

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