As people become more literate with graphs, charts, and other data visualizations, they also need ways to talk about them. You can help them through what I call “wedginess”: giving a name to data shapes.
Check out this chart, for instance.
This chart depicts the numbers of jobs added or lost in the American economy, as measured by the Bureau of Labor Statistics. Political journalist Steve Benen updates this chart every week when new jobs figures are released, but the chart didn’t take off until Rachel Maddow labeled it the “bikini graph” on air in 2010.
“Bikini graph” is pure wedginess. You’re wedgy when you use a word or phrase that describes a geometric or spatial quality of a line or a space on a graph as a stand-in for the phenomenon itself. Not just any word, either, but something with snap. A graph already is a tool for pulling lots of disparate data together, so maybe it seems odd to have a graph stick in the mind. But you still need a way to talk about the lines. So give it a name that’s easy to say and easy to remember.
I call this “wedginess” because the “climate change wedge” was the first example I noticed. The wedge was introduced by Princeton University physicist Robert Socolow and ecologist Stephen Pacala in a 2004 Science article as a way to communicate about approaches to reduce the effects of climate change. Using a certain technology would reduce global CO2 emissions by a certain amount, and the distance between the line marking those projected emissions and the ever-rising line of projected emissions if nothing were done looks like a wedge-shaped chunk. Below is a graph from the National Resource Defense Council that show six of these “stabilization wedges.”
Here are two more wedgy terms that you might have heard:
– Peak oil. Here the “peak” refers to the fall-off in petroleum extraction, not increases in petroleum depletion. The fall-off occurs because the price of extraction has risen too high. As a term, this is highly sticky, despite the fact that it’s easily misinterpreted as oil depletion.
– Fiscal cliff. Contrary to the way “fiscal cliff” was wielded in recent US Congressional debates, the “cliff” originally described a line on a graph: a drastic drop in government revenue. “Fiscal cliff” was used this way by Henry Waxman in 1991 to describe a voter initiative in Oregon that limited property tax increases.
We live in a world of charts and graphs, but my sense is that wedginess doesn’t happen enough. As a metaphor designer, I’m often trying to explain a phenomenon in itself. In some cases, it might be more effective to describe some visualization of data that a group or a field are pointing to.
Why? One reason is that it capitalizes on the picture superiority effect, where concepts are recalled more easily if they’re presented in graphic form. The problem is, talking about graphs or other pictures can be unwieldy, if you’re talking on the phone or without a drawing aid handy: “OK, so on your x axis you have time, and on your y axis CO2 emissions…” Wedginess aids the contagiousness of a message because it puts wheels on the picture superiority effect – it allows you to extend picture superiority into places liken conversations, where there are no pictures.
Ultimately, wedginess helps you mobilize more resources on your side for making your point. Anthropologist Bruno Latour, who has studied the communication practices of scientists since the 1970s, made the same point about charts and graphs: “’You doubt what I say? I’ll show you.’ And, without moving more than a few inches, I unfold in front of your eyes figures, diagrams, plates, texts, silhouettes…” Wedginess is the tool by which you extend the power of these 2-D devices and move them back into the way that people talk and think.
Michael Erard is a linguist and author. He works as a senior researcher for the FrameWorks Institute, where he helps to design and test explanatory metaphors for science translation and social issue reframing. This post was adapted from a post on the FrameWorks’ blog for framers. You can follow him on twitter @michaelerard.
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