The world seems crazy right now. In many ways. Trump, wars, AI getting out of control – and even if it’s not, too many smart people are saying so to completely ignore the possibility.
Huh. Heavy, right?
But only time will tell. So let’s get to our academic business!
There’s a funny problem we all seem to share. We don’t really have a shared understanding of how to measure productivity. And all the traditional ways to measure productivity seem to be…somehow faulty.
The Metrics, they are Broken
Let’s start from a broader picture before getting personal.
The way we measure academic work is in a sorry state. Citations you say? A joke. You get lucky with one paper with the correct buzzwords at the correct time and you’re set for life.
H-index? Come on. You squeeze your name as the glorified middle author in two dozen papers because you’re a social butterfly, and again you’re set for life.
Of course they have some utility, but they’re not really useful. And they take years to develop and measure. Of course, other metrics exist. And, to be fair, the way tenure track boards typically evaluate candidates starts starts to be pretty good: an overall discussion on the applicants across multiple different contribution categories.
But still, our productivity metrics are broken. I’ve seen people who hardly work at all but are seen as productive superstars because they talk the talk and are good at offloading work to others. I’ve also seen people who work like crazy, 16 hours per day, but get nowhere with their own careers. Because they focus on the wrong things.
Ultimately, we just lack the tools to quantify productivity.
And it’s no easy task to do that in the modern workplace.
Productivity has Changed
In the industrial era, productivity was measured by the quantity of output produced in a given amount of time. This worked well for repetitive manual work where output was easily quantified and linear with the time spent on it.
Think of e.g. manufacturing shoes or tools, or, say , mining coal.
Put in time, out comes more units.
Now, think of how complicated work it is what you’re tasked with:
Intangible output
First, your output is not tangible: you can’t see it. You can’t say how many units of time does one unit of your output take. Or at the very minimum, it varies wildly.
And what you can’t see is difficult to perceive as output. You create ideas, make decisions, solve or design solutions to problems – all incredibly difficult to enumerate or quantify.
How much “work” is a solution to a problem?
No clue.
Quality vs. quantity
Second, in our work we deal with quality or success, not so much with quantity. One paper in a great venue is worth more than five in a random predatory venue.
The one accepted grant proposal counts, even though the two failed ones costed you two times more work.
How unfair is that, from the perspective of gauging your productivity?
And over time the quality game can get super annoying: 10 failed ones are still less valuable than 1 accepted one, from an external lens at least. The silent skills development that took place in the process? Yeah, your supervisor might have trouble counting that as productive, even if it was part of your hard work.
Non-linearity
Third, modern work is non-linear. You iterate, you experiment, you retreat to think. You might get a sudden breakthrough after weeks of seemingly “doing nothing” because you just didn’t have the right idea earlier.
Or you take the time to learn new tools, e.g. AI for literature review and qualitative analysis, making your work much faster.
Not linear.
Luck
Fourth, there’s always the luck factor. In the coal mine you had a pick-axe, hands and time. Not much luck involved, just hard work and sweat. In the lab? Well, just for starters, who are your colleagues? What kind of ideas do they contribute? Does your supervisor get lucky with funding so they can hire more brains to help you? Peer review is a gamble always as well: the same paper or proposal can easily get accepted, unchanged, during the next round. Oh, and our dearly beloved currency of citations. Nobody can predict which papers “fly” and why.
The role of luck is huge.
How to Analyse your Productivity
The best way to feel crap after a long day is to know you got nothing productive done.
And these days happen surprisingly often. All the busywork feels like important in the moment, but in reality it’s not.
So, let’s start to think how to use our days better. Here’s a helpful three-part thinking process that will help you recognize what exactly is productive work for you.
Step 1: Recognize you’re in the long game
I want to share a short story.
I once pursued a personal (and pretty luxurious) grant for five years. Every year, it took weeks of labour to get the next version submitted, along with dozens of recommendation letters from collaborators and an ungodly amount of time from my bosses and peers in reviewing my drafts.
Fail after fail after fail after fail. This is a great example of a situation where you can end up asking “was that time productive?“
My story has a happy ending. After all those years, I ended up winning the grant. But if I hadn’t, would I still be wondering if all that time was wasted or not? Because I won it, I can tell inspirational stories about the road to getting there and the lessons learnt, but if I had gotten less lucky during the final round…maybe I would just want to bury the whole experience.
Nobody would for sure had invited me to give talks about winning funding. And yet I know I could have just as well not have won it. Again, luck has a huge role.
So. Win or not, you must start considering even the failures as productive work as long as they are in the correct direction. Academia is not about shovelling snow from pile A to pile B. It’s also not about manufacturing widgets. You’re in the business of building yourself just as much as you’re contributing to your lab and institution.
Failures count as long as they’re part of playing the long game. Related to this, one of my big power principles (very shamelessly borrowed from Jim Rohn) is:
The work gets easier as you work on yourself. And working on yourself is always productive. Learn to distinguish between pointless busywork and work that builds your skills. This will help reduce the number of days you feel bad about not seeing any tangible output.
Step 2: Stop lying to yourself
Cal Newport wrote one of my favourite books on how to think about what we do at the office, Deep Work. Now, he’s got a new one out: Slow Productivity. I’ll admit I haven’t read it yet, but the ideas are much what I have been developing as a concept under the working title Calm Scholarship.
Dialing down on pseudo-productivity can be said in a more direct way: Just stop lying to yourself.
Busywork is not productive work. Much of the work we do is just not necessary or does not help your career.
If you fail to recognize between these different types of work, you will end up being like the academic I mentioned earlier: You grind 16 hours per day, seemingly doing something but getting nowhere.
So, do fewer things but make sure those are things that make sense in your situation.
And for your sanity, don’t fly too close to the sun. Work at your natural pace, focusing on results instead of hours.
But truth be told, I wouldn’t go as far as obsessing over quality. Do that, too early, and it’ll just slow down your work unnecessarily. Take more shots, with good enough quality. I think there’s just too much luck involved in academic work.
Again, a quality proposal will get killed almost as easily as a not-so-incredibly-polished one. Obsess over developing yourself and quality improves automatically.
And while doing that, keep taking those shots.
Step 3: Conduct monthly reviews
I never stop wondering how fast years go by. And months? They’re like snapping fingers. I mean, it was January…like yesterday!
Given how easy it seems to be to immerse ourselves in pseudowork, it only makes sense to do periodical reviews of where are we putting our limited time.
Just don’t be too judgemental. Data about yourself is not criticism, it’s just data on where you’re at.
And without it, you can’t improve.
A monthly review is your best friend here. Set it as a recurring event in your calendar. I offer a set of Notion templates for this too in the PhD Power Trio but, really, any tools work.
A pen and a paper are perfect.
Just take a good critical look at last month’s (intangible) activities as well as tangible outputs. Especially include self-growth activities such as learning new skills, networking, reading articles for getting new ideas, or developing new systems for your work.
Understand that these intangible activities are crucial for that long game.
Also reflect on how much time was spent on pseudowork.
Then take a deep breath…and analyse. What does your past month look like?
Because when you do that, it’s easy to know what to do next.
Plan for the next month.
Based on your reflection, plan for the upcoming month with “real” productivity in mind. Try to think of ways to minimise pseudowork. Perhaps identify a specific skill that you must improve with the long game in mind and focus on that?
Tangible achievements are easy to celebrate. The intangible ones are much harder to give yourself credit for, but they should be understood as part of your productivity.
So, your monthly review is a logical time to celebrate self-growth too.
Establish a Habit
Monthly reviews don’t have to be an extra burden. You should be journaling every week anyway, so might as well just make sure you take a good look at your past work once a month.
It’s not a lot of extra work.
And if you promise yourself that every time when you audit your past work, you commit to eliminating annoying busywork from your life the next month, guess what happens?
These audits become something you look forward to. Because it’s the pointless pseudo-productive work that ruins your days anyway.
Win win.