The problem with workload (and thus work-life balance) in academia is:

You can always do more.

(A thread based on a recent personal epiphany.)
Unlike many other kinds of jobs, when you are a (research-heavy) professor no one tells you exactly what you need to do. Or even how much you need to do. There are things that are wonderful about this kind of freedom. But also, it means that you can always be doing more.
How many research projects should you be doing at any given time? How many papers should you be writing? You might have a personal sense for this, maybe even a rule-of-thumb, maybe even a mentor giving you advice. But whatever N is, it COULD always be N+1.
And what constitutes "should" or "enough" means a lot of different things for different people. Enough to get a job? Enough to get tenure? Enough to feel like you're contributing to a part of science you are passionate about? Enough to make an impact on the world?
Some of us are pretty good at maintaining a reasonable workload. Stopping work at 6pm. Not working weekends. Not working more than N hours a week unless there's a deadline, etc. And you might cap projects based on how much you can accomplish in that time. (I am bad at that part.)
But the thing is, you always COULD decide to do more. Because for the most part, no one is telling you how much you should do but you. You decide what research projects you do, what papers you write, (often) what service obligations you take on.
(I'll also note that, though this can be true for PhD students as well, I think that it is mediated by - hopefully - having an advisor who is giving you more hands on guidance about what is an appropriate amount to do. :) )
Anyway, the problem is, if you CAN do more (and there are people who then DO because of whatever is motivating them towards some "enough" and possibly overworking themselves), even if you know it is unreasonable you still feel a constant low thrum of guilt or concern for not.
Er if you were hoping this thread would end with a brilliant solution, I'm afraid I don't have one. My epiphany was only to identify the root of the low thrum of guilt/concern. (And to be clear, this is NOT me being worried about tenure.) But I guess that's the first step. :)
Seeing how many RTs etc. this thread has gotten, I just want to offer anyone reading this a big virtual hug and say: You are enough. ❤️

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More from @cfiesler

31 May
I'm often struck by how much the foundation of science relies on individual integrity. And typically I feel it's pretty solid. But this kind of garbage is a result of publish-or-perish, bean counting, and the general incentive structures of academia. cacm.acm.org/magazines/2021…
Also when this situation came to light a year ago I went on a whole lengthy tweet-rant about it so I won't repeat myself but here you go. :)
Also to clarify: "is a result of" above should probably be "exacerbated by" because obviously it's a result of when there is a breakdown of individual integrity. Awful people gonna be awful, but there are incentive structures dictating the particular form of awful.
Read 5 tweets
27 May
We read 700+ Reddit papers, analysis described in this (open access!) @SocialMedia_Soc article (led by @moduloone, with @_sgilbert_ @NaiyanJones & @michaelzimmer). Here are some things we know about research that uses data from Reddit! 🧵 journals.sagepub.com/doi/full/10.11…
Unsurprisingly, Reddit as a site of study or data source is on the rise. The first 2 papers we encountered were published in 2010, with a jump to 17 in 2013, and 230 in 2019.
I also think it's really interesting that though computing and related disciplines make up the largest number of journals represented in our dataset of Reddit papers, medicine and health is next - even (just) above social science. Computer Science, Engineering and Math constituted 33% of pu
Read 9 tweets
9 May
I'm increasingly frustrated with "I don't know enough about ethics to include it in my class."

My take: If you e.g. teach an ML class & you actually don't know ANYTHING about ML ethics... learn. Ethics is part of ML. I'm sorry your education was lacking, but now time to learn.
Like, professors should be constantly continuing to learn. I learn new things so that I can teach them all the time. If you say "I don't know enough about ethics" what you probably mean is "I don't care enough about ethics."
I'm not suggesting that if you teach an ML class you need to go read Kant. I'm suggesting go read about ethics IN YOUR FIELD. Ethics is part of that field. So go learn it in the same way you keep up to date on ANYTHING that is new or you don't know!
Read 7 tweets
15 Apr
In honor of PhD application season winding down, here's a tweet-thread-that-should-probably-be-a-blog-post on things I have observed through hearing from a LOT of PhD applicants in many different fields over the past eight months. TL;DR this process can be better. 🧵
Consider things admissions committees or faculty might expect to see in an application: LORs from certain types of people that mention certain things, statements of purpose with certain elements (faculty mentioned, why this program, etc.)

Are applicants TOLD these expectations?
As one critical example, are you in a field where it is common or there is even an expectation that applicants reach out to potential advisors before they submit an application? Is there any reason applicants would know this if they don't already have mentorship in that field?
Read 8 tweets
19 Mar
This thread is for live-tweeting the ethics session at #SIGCSE2021. FIVE papers at @SIGCSE_TS this year about ethics in computer science education! 🧵
First up: "How Students in Computing-Related Majors Distinguish Social Implications of Technology" by Diandra Prioleau et al. at University of Florida.

They presented students with scenarios about AI technology (e.g. recidivism algorithms)...
... and found that their participants could spot social implications, but frequently missed issues of systemic discrimination. But surprisingly: About half of students had never heard of these issues, which points to a gap in computing curriculum. dl.acm.org/doi/10.1145/34…
Read 14 tweets
9 Mar
As you know, I am a fan of @tiktok_us these days, but I need to put them on blast for a bad design choice. Folks interested in content moderation/platform safety, buckle up. This is a story about bad people exploiting a loophole for harassment. We can learn from this. [Thread 🧵]
TikTok has a "block" feature that works similarly to Twitter. If you block someone, you can't see them and they can't see you. This includes comments.

So now we have A (person being harassed) and B (awful person who thinks it's fun to e.g. leave death threats in comments)...
B has figured out that they can comment on A's post and then immediately block A, which then means that A can't see that comment - and in fact doesn't even know it's there since it doesn't show up in their notifications.
Read 8 tweets

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