Rethinking the role of communication in science
by Russ Hodge, copyright 2018
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This article is intended for all the stakeholders in the broad field of science communication: from practicing scientists at all stages of their careers to science students and teachers, journalists, communicators, and educators. It could also be of interest to linguists, cognitive psychologists, and others interested in the connection between thinking and language. I hope it will be read by those responsible for university programs across Europe, because it provides several arguments for making communications training a standard part of their curricula.
Here I bring together ideas that have been dealt with superficially in other pieces (1, 2, 3, 4) on the blog.
This is a rough draft, one of at least three more major parts to come. In it I aim to demonstrate that the relationship between science and communication is far more profound and interesting than we usually consider. The process that most of us go through when we want to communicate well is crucial to clarifying thinking, and it offers tools that could be used much more strategically in posing new scientific questions and interpreting data. To say this as boldly and plainly as possible: learning to communicate well can improve your scientific work – not only because your papers have better grammar, but because it requires a type of thinking that is extremely useful for science.
I do not say this lightly; I know how skeptically most scientists will greet it. That’s fine; I have waited a long time to write this piece because I needed to collect powerful examples to support it and put them together in a convincing way. If you are a scientist, I hope you will recognize aspects of your own thinking in this piece, and feel that it puts words to things that have become your daily habits. It may even surprise you by revealing “mechanisms” of thinking that you have never considered, yet use all the time.
It has been a long road to get here: 20 years of interacting with scientists at all levels of their careers on a daily basis, working together to find didactic approaches to a wide range of problems, and over 30 years as a teacher overall. Yet it wasn’t until a few years ago that I finally decided to confront some frustrating, content-related problems that constantly arise while helping my students and colleagues write, speak, or communicate in other ways about their work . I realized that we didn’t have a very good model to describe and hopefully understand a lot of the problems they encountered. That motivated four years of systematically analyzing these problems. I came to several conclusions:
- Science and communication are profoundly linked at a deeper level than we usually appreciate, which has significant implications for science education programs and the ways individuals, institutes and organisations communicate their work.
- The process of writing or preparing a talk is usually essential in clarifying and organising one’s own scientific thinking.
- This process requires a thoughtful reconsideration of the scientific models related to a project and can expose weaknesses or hidden assumptions that need to be reexamined.
- Every experiment represents a dialogue with models of many types and levels and the results may say something about all of them.
- Becoming aware of hidden connections in the structure of scientific thinking can powerfully affect our interpretation of results and generate important new questions.
- Communication offers an extensive set of tools which can be systematically applied to scientific problems and improve the quality of research.
- Scientific models are highly complex cognitive architectures that individuals construct in their minds and integrate into an “inner laboratory” where the “real science” takes place.
- The only way to examine these architectures is by externalizing them in writing, talks, images, or other modes of representation
- Effectively speaking to the public or non-specialist audiences usually requires seeing familiar systems through new patterns. Doing it well requires a process that can clean up sloppy thinking, help us approach an old theme in a new way, generate new scientific questions and suggest alternative interpretations of experimental results.
I know, the last one’s the big one.
The text starts with a short theoretical introduction. After that I apply the principles it introduces to nine case studies taken from real students’ texts, papers, images and other examples of science communication.
This model is just a beginning, but it has some powerful implications for the way we train scientists and teach them to communicate. It strongly suggests that effective training in these skills should be an integral part of a scientific education early on and continue through a student’s career. But before people start changing their curricula, scientists need to have a convincing model that shows them why it is important, and the method of teaching must be effective. I think this is a start, but it will need to be tested in many formats and teaching environments to be validated and improved.
The model I propose is not comprehensive; I will add another major section on metaphors and patterns in scientific models and a third that specifically explores how these ideas can be practically translated into teaching. I am hoping to work with teachers who are interested in learning the theory and methodology, applying it to other types of science, and becoming multipliers. I think this is the only way to achieve the long-term goal of institutionalising this type of training and ensuring that it becomes a staple of university science curricula throughout Europe.
I need and would greatly appreciate feedback from all stakeholders in this process. Please be as critical as you like; the model has to be tough enough to take it. I will consider all of your comments very carefully, report on them here, and use them to develop better versions of this text, the model it presents, and the teaching that results from it.
Thanks in advance,
Please contact me at firstname.lastname@example.org if you would like to discuss this personally. Also if you are interested in teaching or training in these fields, in learning the methodology yourself, or would like to discuss setting up workshops or a program to implement its ideas.
Russ Hodge, March 2018
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I would like to thank all the scientists who have been such great teachers and given so generously of their time helping me over the past 20 years, the students who continue to inspire this project, the teachers who have been a continual inspiration, and my family, friends, and colleagues present and past for their support.
I would like to particularly thank Prof. James Hartman of the University of Kansas, an extraordinary teacher, lifelong mentor and friend, for setting me on this path so many years ago and stimulating my ideas at exactly the right moments over the years;
Joseph Novak, father of Concept Mapping and one of the most brilliant educators I have ever met, who in a single week at Cold Spring Harbor completely changed my views of the goals of teaching and the methods needed to achieve them;
Jochen Wittbrodt and the COS department at the University of Heidelberg, Gareth Griffiths at the University of Oslo, and Thoralf Niendorf at the MDC for being constantly supportive and serving as the guinea pigs in this crazy endeavour.
3 thoughts on “Ghosts, models and meaning in science”
My first thought was: Well, this is obvious. But as I followed through the details, I realized that the problem is more common and much deeper than I imagined.
And thanks for introducing me to CmapTools – I’ll try them for sure.
The points you make are excellent. However the introduction is waffly. I kept muttering to myself: “Get on with it!” as I read.
Consider refining it down to little more than the points themselves with a brief introduction and acknowledgements.
A point I think worth including is that science is essentially collaborative. The better you communicate, the better the collaboration can be.
Rereading the piece myself after letting it sit for a week… sigh. I completely agree with everything you say. And the point about collaborations is certainly important – especially as they cross interdisciplinary boundaries, where definitions and metaphors clash at a level that often remains hidden. I’m getting lots of very good feedback and will be posting another version of the ideas, set as a dialogue, which is much more concise. If you come across any good examples that would support these points, or challenge them, please let me know. Thanks very much for sticking it out, and let me know what you think of the better version I’ll be producing soon. I’d be interested in whether you’re a scientist or what your involvement in the field is if you care to share.