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In this article, we discuss various public-facing scholarly activities we have engaged in and how these initiatives have reached large audiences to widely spread messages about language and linguistic equity and inclusion. We provide guidance for how to launch, coordinate, and carry out public outreach initiatives and community-engaged research, how to navigate potential pitfalls and position these efforts for success, and how to demonstrate the direct value and relevance of the work. We also offer strategies and advice for other linguists engaging in public outreach endeavors, especially with regard to connecting community-engaged work with teaching and research for maximal impact within the scholarly ecosystem. Community-engaged research and public-facing initiatives are best conceptualized and undertaken in comprehensive, intentional, informed by, and planned in ways that align with best practices in the literature and integrated into the scholarly enterprise. We assert that public-facing work is critical to the relevance and impact of linguistics and higher education. Most importantly, public-facing work that makes insights from research relevant to the public can help advance the broader goal of education for social impact and the public good. 

This article examines linguistic variation in relation to the critical social institution and social domain of education, with an emphasis on linguistic inclusion, focusing on the United States. Education is imbued with power dynamics, and language often serves as a gatekeeping mechanism for students from minoritized backgrounds, which helps create, sustain, and perpetuate educational inequalities. Grounded in this context, the article reviews intersecting factors related to linguistic variation that affect student academic performance. Empirical and applied models of effective partnerships among researchers, educators, and students are presented, which provide road maps to advance linguistic inclusion in schools within the broader social movement for equity in education. 

We present an innovative approach to auto-annotate Expert Defined Linguistic Features (EDLFs) as subsequences in audio time series to improve audio deepfake discernment. In our prior work, these linguistic features – namely pitch, pause, breath, consonant release bursts, and overall audio quality, labeled by experts on the entire audio signal – have been shown to improve detection of audio deepfakes with AI algorithms. We now expand our approach to pilot a way to auto annotate subsequences in the time series that correspond to each EDLF. We developed an ensemble of discords, i.e. anomalies in time series, detected using matrix profiles across multiple discord lengths to identify multiple types of EDLFs. Working closely with linguistic experts, we evaluated where discords overlapped with EDLFs in the audio signal data. Our ensemble method to detect discords across multiple discord lengths achieves much higher accuracy than using individual discord lengths to detect EDLFs. With this approach and domain validation we establish the feasibility of using time series subsequences to capture EDLFs to supplement annotation by domain experts, for improved audio deepfake detection.