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1. Linguistic Variation and Linguistic Inclusion in the US Educational Context

   https://doi.org/10.1146/annurev-linguistics-031120-121546  

   Mallinson, Christine (January 2024, Annual Review of Linguistics)

   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. 

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2. The double identity of linguistic doubling

   https://doi.org/10.1073/pnas.1613749113  

   Berent, Iris; Bat-El, Outi; Brentari, Diane; Dupuis, Amanda; Vaknin-Nusbaum, Vered (November 2016, Proceedings of the National Academy of Sciences)
3. The linguistic position of the Timor-Alor-Pantar languages

   Holton, Gary; Robinson, Laura C. (January 2014, Alor Pantar languages: History and Typology)

   The wider genealogical affiliations of the Timor-Alor-Pantar languages have been the subject of much speculation. These languages are surrounded by unrelated Austronesian languages, and attempts to locate related languages have focused on Papuan languages 800 km or more distant. This chapter draws on typological, pronominal, and especially lexical evidence to examine three hypotheses regarding the higher-level affiliations of the Timor-Alor-Pantar languages: (1) the languages are related to the North Halmaheran (West Papuan) languages; (2) the languages are part of the Trans New Guinea family; and (3) the languages are related to the West Bomberai family, with no link to Trans New-Guinea more broadly. We rely in particular on recent reconstructions of proto-Timor-Alor-Pantar vocabulary (chapter 3). Of the hypotheses evaluated here, we find the most striking similarities between TAP and the West Bomberai family. However, we conclude that the evidence currently available is insufficient to confirm a genealogical relationship with West Bomberai or any other family, and hence, TAP must be considered a family-level isolate. 

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4. Linguistic cues are privileged over non-linguistic cues in young children’s categorization

   https://doi.org/10.1016/j.cogdev.2018.08.007  

   Fairchild, Sarah; Mathis, Ariel; Papafragou, Anna (October 2018, Cognitive Development)
5. Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context

   https://doi.org/10.18653/v1/2023.sigmorphon-1.9  

   Shandilya, Bhargav; Palmer, Alexis (July 2023, Association for Computational Linguistics)

   Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages. In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization. At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities. In this paper, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense. The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence. We first produce silver standard parallel glossed data by automatically labeling the high-resource translation. The model then learns to transform source language morphological labels into output labels for the target language, mediated by a structured linguistic representation layer. We test the model on both low-resource and high-resource languages, and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost. 

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