The diverging dictionaries of science and law
| Date | 01 January 2018 |
| Author | Maya Bielinski,Jason Chin,Helena Likwornik |
| Published date | 01 January 2018 |
| DOI | 10.1177/1365712717725536 |
| Subject Matter | Articles |
Article
The diverging dictionaries
of science and law
Helena Likwornik
University of Toronto Faculty of Law, Toronto, Ontario, Canada
Jason Chin
University of Queensland TC Beirne School of Law, Brisbane, Queensland, Australia
Maya Bielinski
Gilbert’s LLP, Toronto, Ontario, Canada
Abstract
Scientific evidence is easily misunderstood. One of the most insidious instances of mis-
understanding arises when scientific experts and those receiving their evidence assign different
meanings to the same words. We expect scientific evidence to be difficult to understand. What
is unexpected, and often far more difficult to detect, is the incorrect understanding of terms and
phrases that appear familiar. In these circumstances, misunderstandings easily escape notice.
We applied an evidence-based approach to investigating this phenomenon, asking two groups,
one with legal education and one with scientific education, to define five co mmonly-used
phrases with both lay and scientific connotations. We hypothesised that the groups would
significantly diverge in the definitions they provided. Employing a machine learning algorithm
and the ratings of trained coders, we found that lawyers and scientists indeed disagreed over
the meanings of certain terms. Notably, we trained a machine learning algorithm to reliably
classify the authorship of the definitions as scientific or legal, demonstrating that these groups
rely on predictably different lexicons. Our findings have implications for recommending
avoidance of some of these particular words and phrases in favour of terminology that pro-
motes common understanding. And methodologically, we suggest a new way for governmental
and quasi-governmental bodies to study and thereby prevent misunderstandings between the
legal and scientific communities.
Keywords
definitions, different lexicons, evidence-based, expert evidence, law, machine learning
algorithm, misunderstandings, science
Corresponding author:
Helena Likwornik, University of Toronto Faculty of Law, 78 Queens Park, Toronto, Ontario M5S 2C5, Canada.
E-mail: helena.likwornik@utoronto.ca
The International Journalof
Evidence & Proof
2018, Vol. 22(1) 30–44
ªThe Author(s) 2017
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DOI: 10.1177/1365712717725536
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