New tool helps scientists cut the jargon, communicate more clearly

Luckily there’s now a new tool that helps scientists adapt their jargon for a broader audience. Developed by researchers at the Technion – Israel Institute of Technology and Holon Institute of Technology (HIT), the aptly named De-Jargonizer is available for free online.

“Jargon has an excluding quality,” Ayelet Baram-Tsabari, a Technion professor focusing on science and public engagement, told Digital Trends. “It’s like a sign telling the reader, ‘This is not really for you. You won’t understand it anyway.’”

When scientists paste or upload their text into the De-Jargonizer, the system’s algorithm signals words as being either common, uncommon, or jargon, by using black, orange, and red text respectively. In one example, the word “disorder” appears in black, the word “physiology” appears in orange, and “functionally recapitulate” is marked in red.

“I used to be a journalist and wrote a lot about science,” Baram-Tsabari said. “During this time I heard so many complaints from scientists about the unprofessional media, about the ignorant public, about the failing education system that are all responsible for the sorry state of science in the media. But there was only one community [that scientists thought] did not need to change anything – the scientists themselves … And I remember thinking that the only group scientists can directly influence is themselves, and they could use a little help. The De-Jargonizer could help those scientists who wish to write for the public and be understood.”

Along with color codes, the De-Jargonizer offers scores reflecting the frequency of jargon. According to the tools, this article has a score of 90, with 86 percent common words, 9 percent uncommon, and 6 percent jargon. But, to be sure, the system seems a bit too sensitive. It flagged words like “spaceships” and proper nouns as jargon.

“The main idea behind the algorithm is that the language that people use is dynamic, thus words that were considered to be jargon in the past can become familiar with time — for example, cancer and DNA,” said Elad Segev, a lecturer at HIT who also worked on the project. “Thus the algorithm has to be dynamic and to represent the language as it being used by the public. The algorithm is based on the idea that the vocabulary that people read online represents the vocabulary which they are familiar with. Thus words that do not appear, or are very rare in the digital media, can be considered jargon, and scientist should avoid them in writing for the public.”

A paper detailing the project was published last week in the journal Plos One.