Yeah, okay, so I‘m Shay. I‘m a third year PhD student working in solid state physics. I do experimental work. A large part of that is working in a clean room, working in nanofabrication work.

Part of what I do is also simulation based. And a huge part of being PhD student is writing academic papers, so reports based on your work, based on your findings and publications to send it to journals.

Good academic writing is fundamentally based in truth. Academic writing has to be, has has to first and foremost, be about telling you the facts academic writing is not well, it‘s not about trying to sell you on a narrative or a story.

That‘s one of the biggest misnomers surrounding like funded research that‘s funded by companies, funded by individuals who are looking to seek a certain outcome. That is not the role of scientific literature. Scientific literature is about telling you what the test showed. It‘s an interpretation of empirical evidence, of observations, so sort of like the fundamental notion of it should be that if you repeat the tests again, you reach the same conclusions. These are these, like observation, data driven conclusions. You, as the scientists, are almost taking the role of narrator.

You‘re narrating your own data, and you‘re saying the data shows this, and this is why it shows this. So you‘re sort of supporting your you, any statement you make should be supported, and that‘s the reason that we reference heavily. That‘s the reason why, anytime we make we say something and we state it as fact, if we aren‘t displaying our own data to evidence, why we believe that is now fact or scientific fact, which is just one kind of fact. But if we‘re, if we‘re not displaying our own data to say why we‘ve, why we‘ve made this conclusion, then we reference, and we say, well, we are taking the word of X person, and this is their work. You can go and find that. So I suppose, in a roundabout sort of way, you should be able to follow the tree all the way back to the original scientific what you know people, people can ask, you know, people have been asking questions for years, centuries, millennia, even.

And there should be a clear sort of path backwards in time. You know, I‘m saying this today. I made an assumption based on my colleague‘s paper here, assuming that to be true. Their work assumes something they read in a textbook is true. That textbook was talking about something Einstein did, and Einstein was working under the assumption something Newton did. So we can follow the path all the way back to the person who had the first idea, and we can follow back to those initial tests and then say, well, that‘s why what I‘m saying is true.

I think mediocre scientific writing is something that most academics are guilty of, because mediocre scientific writing is almost inadvertent. Great academic writing wants you to, you know, it‘s a skill to be learned on us.
It‘s, you know, we don‘t all produce these great papers, you know, from the start of our careers. So when we‘re working as PhD students, as early career researchers. There‘s a lot that we were not aware of yet. Okay, we have senior academics and principal investigators to read our works and to try and make them as good as they can be, but I think a lot of it inadvertent, because as a as an industry, we collaborate so heavily. We do rely on on our seniors and on the people, yeah, the the the people at the top to guide us, almost to to help show us how can we be good academic writers?

Mm. You know, so mediocre academic writing, kind of, I think it happens more often than not, a lot, you know, I‘m trying to think beyond sort of just making, you know, trip ups in language and in voice, maybe opinionated, academic writing as mediocre, because it‘s, you know, we, we form our, our opinions based on, on fact and sort of, you know, we, but we, we ultimately. We‘re not robots, so we can‘t always write in a way that‘s impartial and robotic.

So we do convey emotion. And also, one of the biggest problems in academia is that we don‘t publish negative conclusions. You know when, if we do, if we do an experiment and we find something is not true, we don‘t usually publish that it‘s not encouraged. You don‘t want to publish something that‘s not true. You also don‘t publish the difficulties that you had along the way. So you don‘t, you know, I, if I write a paper, and this is a great paper that‘s been four years in the work, I don‘t tell you that I spent weeks failing. You know that it all went to shit halfway through, and I thought about giving up. I tell you, it like I got it on the first go in my personal experience, that might be something like writing a paper in device physics. And I say I took the device and I performed this experiment, but you don‘t see the months of trial and error that went into getting the device there. Yeah, so in scientific literature, literature, we don‘t, we don‘t publish our failures. You know, that‘s sort of because great journals don‘t want to publish failure.

They want to publish big headlines and big success stories. But failure is a big part of it. So I think that‘s maybe another part of mediocre scientific writing is that, you know, it‘s, it‘s, it‘s a bit of a demon of academia. But ultimately, our negative conclusions are just as valid as our positive ones, but they don‘t get the same spotlight as we all want to be successful in the end, yeah,

So bad scientific writing, I think, from an obvious point of view, you‘ve got corrupt scientific writing. There‘s, there‘s, there‘s the people who know that they‘re that they‘re faking something.
There‘s the people who, you know that that‘s sort of obvious, isn‘t it? Is that there are people who do give, you know, a bad name, this sort of thing, and are willing to there‘s, there‘s, there‘s levels we describe it in. There‘s tweaking the data, you know, ignoring a ignoring a data point or something that might conflict with your conclusion. There‘s massaging the data, where you push everything, just nudge everything collectively forwards in the direction you want in. And then there‘s just fudging the data all together, where you‘ve just created stuff out of thin air to support your conclusions, which is sort of different levels of moral ambiguity, or not even ambiguity, moral being morally onerous.

So I think that‘s an obvious one for for what makes for really bad academic writing. I guess, in terms of what makes really bad academic writing, there‘s people who there‘s people who have already decided what they‘re going to conclude before they‘ve done the experiments. I think that‘s pretty bad. I mean, it‘s hard to say, it‘s hard to determine whether something is mediocre or bad without bringing something like moral intent into it. But I think if you already know what your narrative is, and you haven‘t got the evidence to support that, yet, you‘re introducing your own bias, and you‘re not taking steps to remove it.

So I think, I think maybe that is what makes for bad scientific writing, is when you have not actually made the effort, the conscious effort, to reject your own bias. Yeah, we are inherently biased. I want to see my experiments succeed. I want to find the to prove my hypothesis. I want to find the objects that I‘m looking for, be it, you know, device, particle, mm. A planet, whatever. But if I am not approaching that from a point of view of No, I must follow my data, then that makes bad scientific writing. I think, yeah, I don‘t know. I can‘t think of much more off the top of my head, maybe egotistical writing. But again, is that, you know, is that bad, or is that just, you know, poor practice.

There are people who definitely write like they are God‘s gift to science. I‘ve got personal experience with these people, and you got to think, well, you know, you‘re not, you‘re not hot shit, you‘re just shit. Yeah, there‘s people who write like this. There‘s people who, I guess, this is calling out journals more than it‘s calling out academics. Yeah, journals really push you to publish big headlines, and I think that makes people jump the gun. That‘s how you end up with attractions, because ultimately, the biggest evidence of bad scientific writing is a retraction, if somebody‘s physically had to take back a paper which which you know really does represent months of work to years of work a lot of the time, and there has to be some experimentation done, because, you know, otherwise, it would never end up in a journal in the first place.

But if you‘re pushed to to publish something groundbreaking, simply because it would be groundbreaking, breaking, that‘s, yeah, I think that‘s a very bad thing. That‘s a very dangerous thing, and it‘s something that gives that has shockwaves throughout an industry, because something we can look at right now is like the room temperature superconductivity fiasco, or what‘s a better word than fiasco, like a controversy is a controversy, isn‘t it? Is? You know, three, four papers now, big papers in leading journals like Nature and Science that have had to be retracted, because fundamentally, there‘s something in there that sort of makes you go, Well, you actually misled us on your conclusions there. Yeah, you know, maybe you didn‘t fudge the date, or maybe you‘ve definitely not. You‘ve definitely used the words room temperature, superconductor for something that really isn‘t what we‘re thinking of when we hear that.

Yeah, and I think that‘s very bad. I think, like I said, that‘s, that‘s partly journal driven, because journals want to reward people who, who goes go hunting for the the big fish, yeah, but, you know, sometimes those big fish aren‘t there, and sometimes they‘re, you know, yeah, sometimes, sometimes they don‘t exist, and sometimes you haven‘t got them yet, but you‘re trying to convince us that that a school of tuna is one giant whale, and it‘s like, well, I don‘t know that‘s a very good analogy at all, but yeah, that‘s misleading. That‘s bad. The one thing I always missed in like physics writing is that you have, like, your papers, you have your journals, but there‘s like, a whole branch of, like, just people thinking, just casually thinking, and it‘s like, if you want to just share it, like, just as a thought, like, you know, you just want to test out a thought. I always feel like, when you do that, yeah, it‘s like, journals jump on it. And it‘s like, you can‘t really, like, as a community, is what I kind of remember.

It‘s like, you can‘t really just brainstorm, just as, like, a whole big thing. Yeah, that‘s a very good point. So the hype, the hype train, is real, yeah, and everyone wants to. So the problem is, it‘s, it‘s an industry of sharks. There‘s, you know, you you‘re genuinely secretive about stuff that you do. So I have, I gave a talk today where I put a figure in. In this talk, it‘s figure that we haven‘t released yet. And the first thought that came into my own when I put it in was, Should I, should I include this, you know, like, and then I remind myself, like, yeah, because my audience is not really going to understand this figure in the first place. And I might explain the couple of things, but they‘re not the people I should be worried about. They‘re not the people. They‘re also not the people who are going to take out the phones as that picture of it. But would I go to a leading to a major conference with collaborators and share this figure? No, because my work‘s not out there yet. So who do I trust? Who can I share this with? Who can I bounce ideas off and brainstorm with without being worried even them being like, Well, I‘m gonna go try that in my lab now. Or, you know, everyone wants to get on, get on the hype train and at. And sort of like share in a big movement in the industry, but there‘s only so much to go around. So people get greedy, and people do steal ideas. It‘s very real.

People steal data. People steal ideas and stuff like that. It does happen a lot in academia, yeah, because it is basically there is also the business side to it, yeah, sure, people, oh, it is a business, and it‘s a pretty cutthroat business to be fair as well, because as academics, we‘re self employed. Fundamental we I say we like I‘m not nearly I‘m a researcher, but not an academic. So academics are self employed. They do have to be their own champions, and almost champion their own business, and say, their own portfolio and say, This is me. This is what I bring to the table. This is why you should fund me. Please give me money to do my research. So they have to, they have to be, they have to be their own champions. And at the same time, they‘re in competitive fields, lots of people are working towards the ideas because, of course, they are. That‘s why there‘s money. There is so that because we‘re working towards common goals.

And there‘s not always enough to go around. Sometimes there is some some industries are very fruitful, and we, you know, know that there is enough research for everyone to be doing their own niche little thing. And some things are just not like that. And if you‘re not collaborating with a big player, who‘s gonna, you know, give you the crumbs. Yeah, super, super made a funny analogy. That‘s like so in my, in my field, which is quantum tech, yeah, obviously, there are industry giants. You know, the reason that my group are not trying to build a quantum computer is because we would never be able to compete with that sort of thing.

You know, even though I think I‘d like to think that we know some pretty smart people, and we have some pretty great capabilities. We don‘t have the the resources or the financial backing of these major players. So he made the analogy, it‘s like, we‘re like vultures, you know, yeah, the big players like Microsoft and IBM, they‘re gonna get, they‘re gonna go for the kill, they‘re gonna take down the carcass. But we, you know, we you know, we might be able to get a little bit of bone marrow from this. We might be able to to get some of the really, you know, nutritional bits off the off the whatever they leave behind, almost, yeah, you know, we know that industry is concerned with that big discovery, the final thing that they‘re gonna be able to sell and take to market, but maybe we can make some interesting scientific advancements along the way, yeah, yeah, that‘s what an aside, yeah, no, this, that was helpful.