Bergelson, Mira, and Andrej Kibrik. Alaskan Russian Through the Prism of the Ninilchik Russian Dictionary Project: ‘Archaeological’ Approach to Language Documentation. Higher School of Economics Research Paper No. WP BRP 55/LNG/2017, National Research University Higher School of Economics, 2017. SSRN, https://ssrn.com/abstract=3082434.
This document describes the project to create the “Dictionary of Ninilchik Russian” by the team of Mira Bergelson, Andrej Kibrik, Wayne Leman, and others. It outlines the guiding principles of the dictionary, which include providing not only meanings and translations but also examples of usage, pictures, cross-references to semantically related words, and phonetic variants as spoken by different speakers. Although the paper version does not include sound files, audio recordings were part of the data collection process.
- Bergelson, Mira, and Andrej A. Kibrik. “Ninilchik Russian in the Broader Context of Alaskan Russian.” Language Contacts in the Circumpolar Region. October 27–29, 2017. Institute of Linguistics RAS, Moscow: Conference Abstracts, edited by O. A. Kazakevich et al., Institute of Linguistics RAS, 2017, p. 9.
- Bergelson, Mira B., and Andrej A. Kibrik. “The Ninilchik Variety of Russian: Linguistic Heritage of Alaska.” Slavica Helsingiensia, vol. 40, Instrumentarium of Linguistics: Sociolinguistic Approaches to Non-Standard Russian, Helsinki University Press, 2010, pp. 299–313.
This article is devoted to the sociolinguistic and historical contexts of the Ninilchik variety of the Russian language, a unique dialect spoken in a remote village in Alaska, founded by Russian settlers who intermarried with the local indigenous population. Ninilchik Russian phonetic and grammatical features are considered in the study, compared with standard Russian, and the potential influence of native languages of Alaska and other Russian dialects is noted. Ultimately, the purpose of the article is to document and analyze this dying dialect, to provide insight into language contacts, dialect variations, and the linguistic legacy of Russia’s presence in North America
- Bergelson, М. B., and Andrej A. Kibrik. “Русский язык на берегах залива Кука: самоидентификация культуры в условиях изоляции” [Russian Language on the Shores of Cook Inlet: Cultural Self-Identification in Isolation]. Vestnik Tomskogo gosudarstvennogo universiteta. Filologiya [Tomsk State University Journal of Philology], no. 54, 2018, pp. 29–41.
- Chang, Tyler A., et al. “When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages.” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2022, pp. 1500–12.
- Erickson, Megan. “Rise of the AI Schoolteacher.” Science and Technology Education, 22 Jan. 2024.
- Kantarovich, Jessica. Russian Contact and Linguistic Variation in Alaska, with Special Attention to Ninilchik Russian. The University of Chicago, 2020.
This source introduces a linguistic study focused on Ninilchik Russian. The paper begins with the historical context of Russian contact in Alaska, which laid the foundation for the dialect’s development. The research then delves into analyzing the distinct features of Ninilchik Russian, examining its lexicon (including archaic Russian words, borrowings from indigenous languages and English, and semantic changes), phonology, word formation, morphology (gender and case), and syntax, often contrasting these with Contemporary Standard Russian.
- Wang, Xinghua, et al. “What Matters in AI-Supported Learning: A Study of Human-AI Interactions in Language Learning Using Cluster Analysis and Epistemic Network Analysis.” Computers & Education, vol. 194, Mar. 2023, article 104703. Elsevier, https://doi.org/10.1016/j.compedu.2022.104703.
The source presents a study examining student interactions with an AI system for learning English by analyzing usage data (frequencies and scores for shadowing, listening, and vocabulary) and reflection essays. The study employed a two-step cluster analysis to categorize students based on their interaction patterns and learning effectiveness, identifying a four-cluster solution. Furthermore, Epistemic Network Analysis (ENA) was utilized to visualize and understand the patterns of students’ retrospective interactions with the AI coach, mapping these patterns across social, cognitive, and teaching presences, as well as learning approaches and motivation. The research identified Cluster 1 as the most effective learner, exhibiting moderate engagement and the highest scores, and their interaction patterns, located in the fourth quadrant of the ENA network, were characterized by a high quality of learner-AI interactions, including elements like affinity and agentic exploration. This methodological approach aimed to provide insights into the relationship between student engagement, interaction patterns within an AI learning environment, and subsequent learning outcomes