Watch your language! Does jargon matter?

The purpose of the present study was to examine elementary (K-6)teacher acceptability of a positive behavioral intervention described in jargon terms and in non-jargon terms during the process of behavioral consultation, measured by the Usage Rating Profile – Intervention Revised (URP-IR). Specifically, the study evaluated whether employed elementary (K-6) teachers’ acceptability ratings differed on a positive behavioral intervention described in jargon versus non-jargon terms. In addition, this study determined whether differences in acceptability existed when considering type of classroom taught (i.e., general education versus special education versus specialized classrooms). One hundred one elementary (K-6) teachers participated in the study. Results indicated that there was no statistically significant difference between elementary (K-6) teacher acceptability of a positive behavioral intervention when described in either jargon versus non-jargon terms. Specifically, the use of jargon did not significantly influence acceptability ratings of the same intervention. Furthermore, there was no statistically significant difference when examining the type of classroom taught and acceptability of the positive behavioral intervention when described in jargon or non-jargon terminology. These findings replicate those of Witt, Moe, et al. (1984) and Rhoades and Kratochwill (1992) who found no difference in acceptability between jargon and non-jargon described interventions. The results provide important implications for consultant interaction with teachers and the use of jargon during the process of behavioral consultation

Watch Your Language: Large Language Models and Content Moderation

Large language models (LLMs) have exploded in popularity due to their ability to perform a wide array of natural language tasks. Text-based content moderation is one LLM use case that has received recent enthusiasm, however, there is little research investigating how LLMs perform in content moderation settings. In this work, we evaluate a suite of modern, commercial LLMs (GPT-3, GPT-3.5, GPT-4) on two common content moderation tasks: rule-based community moderation and toxic content detection. For rule-based community moderation, we construct 95 LLM moderation-engines prompted with rules from 95 Reddit subcommunities and find that LLMs can be effective at rule-based moderation for many communities, achieving a median accuracy of 64% and a median precision of 83%. For toxicity detection, we find that LLMs significantly outperform existing commercially available toxicity classifiers. However, we also find that recent increases in model size add only marginal benefit to toxicity detection, suggesting a potential performance plateau for LLMs on toxicity detection tasks. We conclude by outlining avenues for future work in studying LLMs and content moderation

“Watch Your Language!”: Word Choice in Library Website Usability

Many academic libraries conduct extensive user studies when redesigning their websites, considering characteristics such as design features, information architecture, and link and information placement. One of the less studied aspects impacting library website usability is choice of language. This article presents the results of a usability study conducted at a small Canadian academic library that assessed the impact of word choice on user interactions with its library website. The author provides an overview of the relevant literature and explores the role that word choice, especially on a library website’s home page, can play in user experience

Watch Your Language: An Analysis of Local Government Collective Agreement Harassment Language

This paper examines whether collective agreement language in Canada is working to protect unionized employees from harassment in local government based on an analysis of 250 collective agreements – 200 from local government organizations and 50 from private organizations – and their harassment policies. The findings reveal that as a whole, local government unions are working to protect employees from harassment as compared to private organizations, but public sector unions could also be offering their members much more protection than they currently do