The eternal tug of war between science journalists and scientists. A graphical story.

I am always disappointed by the media coverage on my research area. It forever seems to misrepresent this and forgets to mention that and raises a wrong impression about something. Ask the science journalist and they'll tell you they have to make concessions in accuracy to match the knowledge level of the average reader. The scientist will argue that if the accuracy is too low there's no knowledge to be transferred at all, and that a little knowledge is worse than no knowledge at all. Then the journalist will talk about the need to sell and point to capitalism executed by their editor. In the end everybody is unhappy: The scientists because they're being misrepresented, the journalist because they feel misunderstood, and the editor because they are being blamed for everything.

We can summarize the problem in this graph:

The black curve is the readership as a function of accuracy. Total knowledge transfer is roughly the amount of readers times the information conveyed. An article with very little information might have a large target group, but not much educational value. An article with very much information will be read by few people. The sweet spot, the maximum of the total knowledge transfer as a function of accuracy, lies somewhere in the middle. Problem is that scientists and journalists tend to disagree about where the sweet spot lies.

Scientists are on the average more pessimistic about the total amount of information that can be conveyed to begin with because they do not only believe but know that you cannot really understand their research without getting into the details, yet the details require background knowledge to appreciate. I sometimes hear that scientists wish for more accuracy because they are afraid of the criticism of their colleagues, but I think this is nonsense. Their colleagues will assume that the journalist is responsible for lack of accuracy, not the scientist. No, I think they want more accuracy because they correctly know it is important and because if one is familiar with a topic one tends to lose perspective on how difficult it once was to understand. They want, in short, an article they themselves would find interesting to read.

So it seems this tug of war is unavoidable, but let us have a look at the underlying assumptions.

To begin with I've assumed that science writers and scientists likewise want to maximize information transfer and not simply readership, which would push the sweet spot towards the end of no information at all. That's a rosy world-view disregarding the power of clicks, but in my impression it's what most science journalists actually wish for.

One big assumption is that most readers have very little knowledge about the topic, which is why the readership curve peaks towards the low accuracy end. This is not the case for other topics. Think for example of the sports section. It usually just assumes that the readers know the basic rules and moves of the games and journalists do not hesitate to comment extensively on these moves. For somebody like me, whose complete knowledge about basketball is that a ball has to go into a basket, the sports pages aren't only uninteresting but impenetrable vocabulary. However, most people seem to bring more knowledge than that and thus the journalists don't hesitate assuming it.

If we break down the readership by knowledge level, for scientific topics it will look somewhat like shown in the figure below. The higher the knowledge, the more details the reader can digest, but the fewer readers there are.

Another assumption is that this background level is basically fixed and readers can't learn. This is my great frustration with science journalism, that the readership is rarely if ever exposed to the real science and thus the background knowledge never increases. Readers don't ever hear the technical terms, don't see the equations, and aren't explained the figures. I think that popular science reporting just shouldn't aim at meeting people in their comfort zone, at the sweet spot, because the long-term impact is nil. But that again hits the wall of must-sell.

The assumption that I want to focus on here is that the accuracy of an article is a variable independent of the reader themself. This is mostly true for print media because the content is essentially static and not customizable. However, for online content it is possible to offer different levels of detail according to the reader's background. If I read popular science articles in fields I do not work in myself, I find it very annoying if they are so dumbed down that I can't make a match to the scientific literature, because technical terms and references are missing.  It's not that I do not appreciate the explanation at a low technical level, because without it I wouldn't have been interested to begin with. But if I am interested in a topic, I'd like to have a guide to find out more.

So then let us look at the readership as a function of knowledge and accuracy. This makes a three-dimensional graph roughly like the one below.

If you have a fixed accuracy, the readership you get is the integral over the knowledge-axis in the direction of the white arrow. This gives you back the black curve in the first graph. However, if accuracy is adjustable to meet the knowledge level, readers can pick their sweet spot themselves, which is along the dotted line in the graph. If this match is made, then the readership is no longer dependent on the accuracy, but just depends on the number of people at any different knowledge background. The total readership you get is the sum of all those.

How much larger this total readership is than the readership in the sweet spot of fixed accuracy depends on many variables. To begin with it depends on the readers' flexibility of accepting accuracy that is either too low or too high for them. It also depends on how much they like the customization and how well that works etc. But I'm a theoretician, so let me not try to be too realistic. Instead, I want to ask how that might be possible to do.

A continuous level of accuracy will most likely remain impossible, but a system with a few layers - call them beginner, advanced, pro - would already make a big difference. One simple way towards this would be to allow the frustrated scientist whose details got scraped to add explanations and references in a way that readers can access them when they wish. This would also have the benefit of not putting more load on the journalist.

So I am cautiously hopeful: Maybe technology will one day end the eternal tug of war between scientist and science writers.