The first dictionary of Umpithamu, one of Australia’s indigenous languages, has been published, after a long compilation process of 17 years and more tragically, after the deaths of its two last active speakers. Dr Jean-Christophe Verstraete worked with the two, a pair of sisters, hand-in-hand, to preserve the language, one of five spoken by the Lama Lama people of north-east Australia, in the form of a dictionary and a companion audio app with the recordings of the sisters’ voices. Though most members of younger generations have not grown up with the language, it is hoped that the 600 copies of the dictionary gifted to the community will allow them to learn Umpithamu, reclaim their linguistic heritage and keep the sisters’ legacies alive by restoring intergenerational transmission.
A language revitalization project that will help language apprentices and linguists alike has been launched by the Tunica-Biloxi Tribe of Louisiana’s Language and Cultural Revitalization Program and the American Philosophical Society. This project will compile and digitize primary manuscripts in the Tunica language, supplemented with additional information from community language experts and linguists, making them more accessible and informative. Once complete, the creators are hoping this valuable resource will help teach others about the Tunica language, history, and culture. This project can also act as a model for similar work with manuscripts and texts in other endangered Indigenous languages.
This article highlights connections between name, identity and culture and what it means for people with different ethnic backgrounds. People who claim their given name are often faced with micro-aggressions tied to a deep rooted history of racism. Others decide to go by Anglicized names for the sake of ease in pronunciation, a decision sometimes met with criticism, based on the desire to preserve immigrant culture and identity. UCLA Linguistics Professor, Jessica Rett explains that language, like fashion, can be used to signify a cultural identity in different situations and in different ways. When people decide how to be addressed, they can choose to highlight an aspect of their identity with a certain group at a certain time.
Body language expert Patti Wood takes a close look at how the two presidential candidates presented themselves during their first debate. President Trump has been known to be aggressive and angry in his speeches and, according to Wood, he stayed true to his brand. Trump took distinctly combative body language by looking at Biden directly while he interrupted and turning his whole upper body towards the democratic candidate. And Biden responded with smiles and eye contact with the camera. So the question is, did Biden succeed in presenting a calm contrast to Trump?
Commentaries and Features
In this radio story, we learn about the origins and development of Nicaraguan Sign Language (NSL), or Idioma de Señas de Nicaragua. NSL emerged spontaneously in the 1980s, at first as a system of gestures amongst deaf children in schools, who were taught Spanish and to lip-read. The Nicaraguan Ministry of Education had invited MIT-trained linguist Judy Shepard-Kegl to observe classes in Managua, and over time she noticed “reverse fluency” in action: younger children were more fluent than older speakers of this sign language. To linguists like Shepard-Kegl, the emergence of NSL supports the belief that humans have an innate capacity for language.
Climate change education and advocacy go hand in hand with language revitalization in this article about Iñupiaq learner and teacher Annauk Denise Olin. Olin has been working with her community in Shishmaref, Alaska to ensure elders have a voice to convey changing climate conditions in their own language, as well as to seek federal support in relocating amidst the drastically altered climate. In addition, Olin talks about the importance of indigenous communities learning and teaching their language, and how this ties to identity and culture. With the added knowledge of linguistic tools and strategies she is developing language programs that will be beneficial to her community and their future.
Have you ever wondered about the factors that influence the types of results you get from search engines?
When computational models are biased into learning language from bodies of texts that contain stereotypes, this would be problematic or even harmful for specific communities: hate speech detectors have proven to be biased against African American Vernacular English (AAVE), or automatic text generators have inadvertently generated racist statements. A team of researchers from New York University has identified a set of cultural stereotypes that AI models pick up when they learn English. Through this project, they hope to identify and quantify the bias at the level of the language model, such that issues with applications and language technology can be avoided.