Post by Caleb Aldridge.—I’ve been revisiting a book by Joshua Schimel entitled, “Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded” (Oxford University Press, 2012). (Click here to preview this book on Google). While skimming through I became reacquainted with a story framing tool that I should use more and believe it can be useful to others.
But first let’s think about what makes a good scientific story. One of the key reasons we write scientific and scholarly articles is to share the knowledge and understanding we gained from the data and information we gathered and analyzed. After all, science uses structured, evidence-based processes to making sense of fragmented pieces of information.
We all want to effectively communicate our new information and understanding to others. This sometimes includes policymakers and key authorities. Schimel turns to the mnemonic, SUCCES, created by Chip and Dan Heath in “Made to Stick” (Random House, 2007). The basic ideas behind SUCCES are to make a story:
- S: Simple
- U: Unexpected
- C: Concrete
- C: Credible
- E: Emotional
- S: Stories
Simple stories are easier to remember and simple ideas have power. Good papers present something novel that fills the knowledge gap. Novel papers will likely do well to in providing solid evidence to their abstract model or theory—there is healthy tension between what we imagine and our data. The credibility of our data and ideas are also important to crafting a SUCCES story. It is important to build a bridge of logic from what we know to what you propose or hypothesize. Emotion can be viewed as a negative influence on science but a driving emotion in a good science is curiosity. Keep your audience curious from the engaging question to the satisfying conclusion. And lastly, see your work as chapters of a larger narrative by finding the units within single papers that can be packaged into coherent modules.
It will take some practice and trial and error before consistently writing SUCCES stories, but one tool that I’ve found useful for framing my stories is the message box (Fig. 1). This tool is discussed by Schimel from the message box concept described by Nancy Baron in “Escaping the Ivory Tower: A Guide to Making Your Science Matter” (Island Press, 2010).
Figure 1. The message box. Modified from Writing Science by J. Schimel © 2012 Oxford University Press.
This graphical tool helps you as the storyteller identify the overall issue (A) and audience (B), which are important as overarching themes in your story. The problem is then defined. This should be a short statement or question that reminds you of what is engaging and challenging about your work. It’s also important to recognize and address your audiences ‘so what?’ question. Be sure to reflect on how this new information and understanding is concrete, credible, and even emotional. Building that concreteness and credibility comes through your solution, or the actual research you conducted. The solution part of the message box should ‘one-liner’ to how you address the problem. Finally, how does your audience benefit from your solution to the problem? Does is satisfy, at least in-part, the ‘so what?’ And, did we arrive at this resolution logically from your solution?
Schimel provides a good example on page 201 (Fig. 2).
Figure 2. A message box for studying microbial activity in artic tundra soils during winter. Adapted from Writing Science by J. Schimel © 2012 Oxford University Press.
Our lab, the Fishery Management & Aquatic Conservation Lab, focuses a lot on using scientific information and understanding to support objectives based decision making. I’ve written a little about connecting experimental design and decision here and here, but writing about decision making in a natural sciences field can be challenging and awkward. Most readers expect the story to focus on variables that influence the system. Decision stories focus more on applying the understanding of the system to meet management and conservation objectives. Sometimes this includes ways to control or manipulate influential variables and quantifying the uncertainty of that manipulation, but sometimes it comes down to the realization of what we can’t control or how little influence we actual have on the outcomes. The take away may even be the identification of where the next study should focus (i.e., reducible uncertainty).
To help myself and hopefully others in our lab/area of research I modified the message box with elements of structured decision making (Fig. 3).
Figure 3. The message box with elements of structured decision making. Modified from Writing Science by J. Schimel © 2012 Oxford University Press.
This ‘message box for decision studies’ is version 0.0.1 so to speak. I’m sure there are parts missing and perhaps elements that should not have been included, but I’m hoping it helps us to think about telling decision stories.