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. 2020 Sep:47:100895.
doi: 10.1016/j.jeap.2020.100895. Epub 2020 Jul 28.

Preliminary evidence of linguistic bias in academic reviewing

Affiliations

Preliminary evidence of linguistic bias in academic reviewing

Stephen Politzer-Ahles et al. J Engl Acad Purp. 2020 Sep.

Abstract

Recent years have seen a spirited debate over whether there is linguistic injustice in academic publishing. One way that linguistic injustice might occur is if gatekeepers (e.g., peer reviewers and editors) judge the scholarly quality of academic writing more harshly if the writing does not meet expectations for international academic English, even if the content is good. We tested this with a randomized control study in which scholars judged the scientific quality of several scientific abstracts. Each abstract had two versions with identical scientific content, such that the language in one version conformed to standards for international academic English, and the language in the other version did not (but was still comprehensible). While the data are preliminary and the effects statistically inconclusive, both pre-registered and exploratory analyses of the data suggest that scholars may give abstracts lower ratings of scientific quality when the writing does not conform to standards of international academic English. These results suggest that linguistic bias may occur in academic peer reviewing and motivate further study to better understand and address this phenomenon.

Keywords: Academic publishing; Implicit bias; Linguistic injustice; Peer review.

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Figures

Fig. 1.
Fig. 1.
Proportion of participants giving each rating to the “standard English” abstracts (solid line) and to the “non-standard English” abstracts (dashed line).
Fig. 2.
Fig. 2.
For each version of each abstract (only including abstracts 1 and 3), the proportion of participants giving each rating.
Fig. 3.
Fig. 3.
“Mean” ratings (based on treating the ordinal ratings as continuous data) for each type of abstract, in each list; see text for explanation. The text on the graph shows the “mean” rating for each individual abstract that was included in that abstract type (i.e., it can be seen that Abstract 1 received higher ratings than Abstract 3, and Abstract 4 received higher ratings than Abstract 2).

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