Six Research Contribution Narratives Editors Recognize (Full Transcript)

[00:00:00] Speaker 1: Today I want to show you something that once you see, it's hard to unsee. Just like literature has a small number of master narratives and storylines, research does too. And why it matters for you is because your paper right now probably fits one of the six master research archetypes I'm going to talk about today. You just might not have ever thought about it or been consciously aware of which one. I certainly wasn't until I've reviewed as an editor, a professor, thousands of manuscripts on both sides of the publishing process. So when you see and you can spot which master narrative you're deploying, it becomes so much easier to sharpen your contribution, to articulate your paper's value to researchers and editors so that they get it, and also know which narrative frame and structure to adopt, especially in the introduction section, which can be one of the hardest ones to write, so that your key narrative really emerges using a structure that's the clearest and strongest for it. So today's video isn't about going into formulas and templates on how to write different sections. We've got those in our other videos on my channel, which is really fantastic, whether you're a complete beginner or an experienced professor, because it takes the guesswork out, sets up a publishing system that you can rinse and repeat. This video is about understanding the basic story your research is telling. So before going into these six master narratives, let me start with an analogy so you get the idea. If you ever watch romantic comedies, I don't do a whole lot of, but especially those Hallmark style ones that come around at Christmas, you'll notice something funny with them. They're all different, but they're also all the same too. They all follow the same kind of formula. And so you usually have a setup where the woman is leaving her familiar surroundings, could be for a trip or a new job. She meets a cute guy. There's some kind of forced proximity where they're put together, and then a misunderstanding happens. And that understanding creates a moment of tension that could break them or bring them together. And somehow the woman usually has to overcome fear or own limiting beliefs or constraints of her past environment to get a deep realization. And that brings them together. And there's happily ever after. You can play with the ingredients, change the town, the setting, the job, the season. Christmas is common. But the narrative structure is broadly following that same arc. And we don't find that boring. We actually find it comforting and legible and emotionally satisfying. So here's the key insight. Editors read research the same way. Not emotionally like a rom-com, but cognitively. They are looking to quickly see what's the big contribution narrative here? What does this paper add? And why do I want it for my journal? And so over the years, reading thousands of papers on both sides of peer review, I've seen empirical research tends to fall into a small number of these master contribution narratives. So what I'm going to do is I'm going to show you examples of these narratives from my own research across social sciences, natural sciences, health sciences, just to illustrate the point. But I want you to think about where your paper might fit into these archetypes. So you can sharpen your own contribution. Again, formulas, templates live elsewhere. So the first big archetype. This is on quantifying costs and benefits of something. It could be quantifying the good and the bad of something. It doesn't have to be narrow economic cost and the sense of money. This also applies to a lot of different interventions. This is especially common in research that's trying to test if a program or evaluation works well. This can be done with a range of different methods in the lab. It can be quantifying with statistical models. It can be qualitative research to document something is good or bad, has harm. And sometimes it can be trying to precisely measure how big an effect is and in whom. And so it's often cost or benefits, harms or gains, risk factors, prevalence, and often intervention or policy evaluation. Editors really like these papers because they strengthen the core evidence base and can support action. I find this is especially powerful and really catapults to the top tier in social sciences or medicine or biology if you're able to demonstrate something that people believed but haven't been able to show before. So let me show you two examples quickly from my work. I've got one. And this one looked at COVID lockdowns and their impact on mental health. Something that many of you have lived through, believed would be the case, but wasn't easy to show in a convincing, demonstrated, causal sense. And we had a really clean design. Note here, we had a great method. That wasn't really our innovation. It was more what the payoff was to the story. Let me give you another example. In this paper I pulled up here, there were concerns that budget cuts that the UK implemented after the 2010 financial crisis could have harms, could cause people to suffer. The question was, how bad was it? And to what extent? Was that really the case? Or was it exaggerated? And here we showed how these budget cuts were leading to rises in children's food insecurity and hunger and the proliferation of food banks across the UK. So I would say a large amount of research fits into this first archetype. And be aware, some of these archetypes can combine, but usually one is going to be the most dominant and the frame that you want to lead with to avoid muddling your story or causing unnecessary confusion. Second narrative is about revealing something we've been missing. Or shining a spotlight on something. Or sometimes, as an epidemiologist, we might do this on a dark corner of society. The core question here that underpins this narrative is what important thing has been invisible, misclassified, or overlooked. So it could be a new gene or biomarker that, for example, I don't know, circadian rest activity rhythms can predict early signs of dementia. Or shining a spotlight, like I said before, on a vulnerable high-risk population, such as some of our work that showed a very high risk of in-work poverty. People who were working a lot, but still staying poor. Other asymptomatic or silent cases, misspecified targets. So this is a very powerful approach. And sometimes this can really be unlocked by new methods. And sometimes people here mistake that, well, the new method is the contribution. And it can be. But really, the payoff to this narrative is, well, what does that method enable us to see? So let me give you one example of this. So this was some of the work that we did in India. Had a research team out in Delhi for some time. There was a thought that there was a boy preference. And we wanted to look at this behaviorally as well. Not just if people, there were sex-selective abortions at birth. I don't want to get into the literature. But the point was here, we looked at whether girls had a nutritional disadvantage compared with boys in India. If that parents were giving more food, allocating more breastfeeding, nutritional resources to boys. And it was just shining a light on a hidden consequence. If you want to check out the paper, you can easily find me on Google Scholar. But this is an example of revealing a hidden phenomenon. Okay. Narrative number three. And I say this is really common in more qualitative research. And especially in chemistry, in a core biology work. And that's explaining how something works. Really elucidating mechanisms, causal chains, and pathways. It's really getting at through what process does this lead to that. And so they're looking at mediators, system dynamics, processes. Again, this occurs across fields. There are just certain fields where these narratives are more dominant than others. And they really get into the question of how. And this can be particularly valuable sometimes if there's a black box. Like we can see you put this and you get this result. There's a lot of drugs that work that way in medicine as well. But it's kind of black box because we don't know what's happening inside. Why it works in a way sometimes large language models in AI. That way we people try to unpack. Why are they working so well? What's going on inside the black box? And explainable AI's whole attempt to get at that. I digress. But I've got two examples from my own work that really bring this narrative into focus. So here's one from back in the day. Very first paper I ever published as an undergraduate, by the way. I always had a love for research. Just infectious curiosity. But I digress. I was terrible in the lab. I had to do a bunch of pipetting for this paper. And realized I did not want to go down the biomedical path in the lab. So this was just showing a mechanism of how drug resistance was mediated. And a certain drug was being processed. I don't want to go into this. But you get the idea. This is a very common story. Turning to a more social science oriented example. Here I've got a qualitative study of factors impacting the access to institutional delivery. Important to get mothers access to proper health facilities where they can have safe births. And really went deep in trying to understand a thick description of what was going on. Why the programs were enabling or not enabling mothers to access care. And those kinds of thick descriptions of mechanisms and explanations that connect the why and the how is really powerful in qualitative studies. Okay next big narrative. This is particularly common I'd say in economics and political science. Sometimes broader humanities as well. Is when you try to resolve a theoretical dilemma. But often you do so in an empirical way. And it's to try to figure out well which explanation is right. Under what conditions. And so you're testing competing theories. Identifying boundaries. Conditions where they work don't work. Confirming or falsifying these theoretical explanations. Maybe trying to shed new light on a long-standing debate. Usually these aren't building theory from scratch. They're trying to resolve tension in an existing theory. And sometimes they can conclude with some theory building up at the end. So I've got a couple examples here. So as you can see I've got one that was looking at economic growth trajectories in post-communist countries. After the Berlin Wall fell and the Soviet Union disintegrated. And looked at three kinds of different theories and arguments for why that took place. And shed some light on which of these theories was more accurate. And appropriate to explain the empirical phenomena. And I've got another one here. Looking at the population causes of leading chronic non-communicable diseases. Like heart disease, diabetes, common cancers. A lot of work had been done at the individual level. But there was a bit of difficulty translating those individual factors. To explain why some populations had different rates than others of chronic diseases. And so again it went through leading explanations. At the population level of what was going on. And made a judgment on which of these was more prominent or powerful. So that takes me to the fifth narrative. Which is actually closely related. It often does marry up in even a couple of the examples that I showed you. Like that economic growth one. They can link together. And that is solving a puzzle. That puzzle solver is really powerful in science. It's a frame people immediately can get drawn into. It's one that I really love deploying in social science. And some of my biggest hit papers have adopted that narrative arc. And really the question here is you have some on the surface contradictory facts. Evidence that's mixed or confusing. That just demands resolution. And so these puzzle papers really try to look at some kind of variation across context. Or they dig into a deep paradox. They often motivate theory. Because they point out well our existing theories might not count for this puzzle. Or we're missing a mechanism or explanatory factor. And editors do like these. Because they do reduce confusion in fragmented literatures. Sometimes to solve these puzzles it can be very powerful. If you lift a method or technique from one literature. And apply it for the first time to this new literature. But again it's all tethered by the payoff isn't the method. It's the payoff is the explanatory power. I've got an example of where I did this here. So here in the Lancet during the transition from communism to capitalism. There was a huge mortality crisis. One of the worst in a time of peace. Outside of famine. But it was a mystery of why it happened. It was concentrated in working-age men. Not the weaker elderly vulnerable groups like infants. And so we introduced a new explanation to account for the puzzling rise. And also why that rise was seen in some countries but not in others. Again the point here is not my specific paper. It's just to show you that this is how this puzzle could look in practice. And I hope you're thinking as you go through these. Does my paper fall into any of these? Because really you want your paper to have a big defining message. That others can spot and see instantly. That's not only how you get accepted. It's how you get out of zero citation jail. Because the medium paper in the first two years gets cited zero times. I don't want you to be in zero citation jail. Okay final one. This one's also very high status and powerful. And that is exposing unintended consequences or counterintuitive effects. I personally really love this one. Especially one for me. I've always been motivated by doing research that has impact. It's not just a clever counterintuitive finding. Which sometimes social scientists fetishize and love. But one that also can be clever counterintuitive and really matter. To something people value and care about. And this is usually going into. Well what is this policy or program doing beyond its intended purpose? So you get policy backfires. You can get environmental fields. You can get greenwashing kinds of behavior. Different moral hazards. Outcomes that just aren't intuitive. Or you wouldn't have thought of. And papers often sit at the end of a research arc. They're really a payoff to your work. They really challenge assumptions. And force people to rethink what they thought they knew. So I've got two examples here. So here's one where we're looking at the impact of recessions on health. And showing some counterintuitive findings. That recessions could be healthy from fewer cars on the road. And so fewer road traffic accidents. It also showed an important payoff in this paper. That suicides were just not an inevitable consequence of recessions. But that in some countries when there were recessions. Like in Sweden. Suicides didn't change at all. So had some really important counterintuitive findings. Another example of this. Fits a bit with the prior narrative on the puzzle. Was to look at the role of budget cuts in the historical rise of the Nazi party. And we've done a series of papers. Looking how some certain economic policies. By creating population insecurity and instability. Could lead to the rise of populism and radical politics. But I don't want to get into that. But the point is here. We were able to demonstrate that deep budget cuts. That caused a lot of population suffering. Played a direct role. Through voting patterns. Of the rise and increasing vote shares to the Nazi party in Germany. So just stitching this back together. Here's the key thing to understand. Is that great papers often do combine these narratives. You can see elements of that in my own highly cited work. That I've drawn on here. But they almost always lead with one. And you know just like in a rom-com. You got a blend of comedy and drama. You still have a narrative arc. You still know what kind of movie you're watching. It's still clear what kind of story you're telling. And so if reviewers can't tell what kind of paper yours is. They struggle more. They have to work a whole lot harder to evaluate it. And one of the principles we work from. Is to make it as easy as possible. On your editors. Your reviewers. And your readers. To see your message. The solidity and strength of your findings. And why they're important. So I guess the question is. That I can leave you with. Is if you had to finish the sentence. Like this paper contributes by. Which of these narratives would you name? And chances are once you see it. You'll realize your contribution was there all along. It just needed some sharpening. And that often can make the critical difference. Between a paper that stalls. And gets dusk rejected. And one that lands. And turns into a classic home run. Hope you enjoyed this video. If you want more publishing tips. You're not going to want to miss the video. I've got for you right here. See you in the next one guys.