Cargando…

Automatic jargon identifier for scientists engaging with the public and science communication educators

Scientists are required to communicate science and research not only to other experts in the field, but also to scientists and experts from other fields, as well as to the public and policymakers. One fundamental suggestion when communicating with non-experts is to avoid professional jargon. However...

Descripción completa

Detalles Bibliográficos
Autores principales: Rakedzon, Tzipora, Segev, Elad, Chapnik, Noam, Yosef, Roy, Baram-Tsabari, Ayelet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549884/
https://www.ncbi.nlm.nih.gov/pubmed/28792945
http://dx.doi.org/10.1371/journal.pone.0181742
_version_ 1783256038491815936
author Rakedzon, Tzipora
Segev, Elad
Chapnik, Noam
Yosef, Roy
Baram-Tsabari, Ayelet
author_facet Rakedzon, Tzipora
Segev, Elad
Chapnik, Noam
Yosef, Roy
Baram-Tsabari, Ayelet
author_sort Rakedzon, Tzipora
collection PubMed
description Scientists are required to communicate science and research not only to other experts in the field, but also to scientists and experts from other fields, as well as to the public and policymakers. One fundamental suggestion when communicating with non-experts is to avoid professional jargon. However, because they are trained to speak with highly specialized language, avoiding jargon is difficult for scientists, and there is no standard to guide scientists in adjusting their messages. In this research project, we present the development and validation of the data produced by an up-to-date, scientist-friendly program for identifying jargon in popular written texts, based on a corpus of over 90 million words published in the BBC site during the years 2012–2015. The validation of results by the jargon identifier, the De-jargonizer, involved three mini studies: (1) comparison and correlation with existing frequency word lists in the literature; (2) a comparison with previous research on spoken language jargon use in TED transcripts of non-science lectures, TED transcripts of science lectures and transcripts of academic science lectures; and (3) a test of 5,000 pairs of published research abstracts and lay reader summaries describing the same article from the journals PLOS Computational Biology and PLOS Genetics. Validation procedures showed that the data classification of the De-jargonizer significantly correlates with existing frequency word lists, replicates similar jargon differences in previous studies on scientific versus general lectures, and identifies significant differences in jargon use between abstracts and lay summaries. As expected, more jargon was found in the academic abstracts than lay summaries; however, the percentage of jargon in the lay summaries exceeded the amount recommended for the public to understand the text. Thus, the De-jargonizer can help scientists identify problematic jargon when communicating science to non-experts, and be implemented by science communication instructors when evaluating the effectiveness and jargon use of participants in science communication workshops and programs.
format Online
Article
Text
id pubmed-5549884
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-55498842017-08-15 Automatic jargon identifier for scientists engaging with the public and science communication educators Rakedzon, Tzipora Segev, Elad Chapnik, Noam Yosef, Roy Baram-Tsabari, Ayelet PLoS One Research Article Scientists are required to communicate science and research not only to other experts in the field, but also to scientists and experts from other fields, as well as to the public and policymakers. One fundamental suggestion when communicating with non-experts is to avoid professional jargon. However, because they are trained to speak with highly specialized language, avoiding jargon is difficult for scientists, and there is no standard to guide scientists in adjusting their messages. In this research project, we present the development and validation of the data produced by an up-to-date, scientist-friendly program for identifying jargon in popular written texts, based on a corpus of over 90 million words published in the BBC site during the years 2012–2015. The validation of results by the jargon identifier, the De-jargonizer, involved three mini studies: (1) comparison and correlation with existing frequency word lists in the literature; (2) a comparison with previous research on spoken language jargon use in TED transcripts of non-science lectures, TED transcripts of science lectures and transcripts of academic science lectures; and (3) a test of 5,000 pairs of published research abstracts and lay reader summaries describing the same article from the journals PLOS Computational Biology and PLOS Genetics. Validation procedures showed that the data classification of the De-jargonizer significantly correlates with existing frequency word lists, replicates similar jargon differences in previous studies on scientific versus general lectures, and identifies significant differences in jargon use between abstracts and lay summaries. As expected, more jargon was found in the academic abstracts than lay summaries; however, the percentage of jargon in the lay summaries exceeded the amount recommended for the public to understand the text. Thus, the De-jargonizer can help scientists identify problematic jargon when communicating science to non-experts, and be implemented by science communication instructors when evaluating the effectiveness and jargon use of participants in science communication workshops and programs. Public Library of Science 2017-08-09 /pmc/articles/PMC5549884/ /pubmed/28792945 http://dx.doi.org/10.1371/journal.pone.0181742 Text en © 2017 Rakedzon et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rakedzon, Tzipora
Segev, Elad
Chapnik, Noam
Yosef, Roy
Baram-Tsabari, Ayelet
Automatic jargon identifier for scientists engaging with the public and science communication educators
title Automatic jargon identifier for scientists engaging with the public and science communication educators
title_full Automatic jargon identifier for scientists engaging with the public and science communication educators
title_fullStr Automatic jargon identifier for scientists engaging with the public and science communication educators
title_full_unstemmed Automatic jargon identifier for scientists engaging with the public and science communication educators
title_short Automatic jargon identifier for scientists engaging with the public and science communication educators
title_sort automatic jargon identifier for scientists engaging with the public and science communication educators
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5549884/
https://www.ncbi.nlm.nih.gov/pubmed/28792945
http://dx.doi.org/10.1371/journal.pone.0181742
work_keys_str_mv AT rakedzontzipora automaticjargonidentifierforscientistsengagingwiththepublicandsciencecommunicationeducators
AT segevelad automaticjargonidentifierforscientistsengagingwiththepublicandsciencecommunicationeducators
AT chapniknoam automaticjargonidentifierforscientistsengagingwiththepublicandsciencecommunicationeducators
AT yosefroy automaticjargonidentifierforscientistsengagingwiththepublicandsciencecommunicationeducators
AT baramtsabariayelet automaticjargonidentifierforscientistsengagingwiththepublicandsciencecommunicationeducators