Karen Martin and Dane Malenfant have been named Rathlyn Fellowship recipients for the 2023-24 year. This fellowship of $12,500 is awarded yearly by the Indigenous Studies Program to support Indigenous graduate students in their pursuit of excellence in research.
Revitalizing the Mi’gmaw language
Karen Martin, a Mi’gmaw student from Listuguj, is in her second year of a master’s degree in the program of Education and Society. Martin’s thesis aims to create a Mi’gmaw Verb Conjugation Tool to support language revitalization efforts in Listuguj. Focusing on intransitive verbs, commonly used in early-level language learning, the project will identify and document these verbs in past, present, and future tense.
By consulting with expert language speakers, and elders, Martin’s project will establish a database format reference tool that can aid in curriculum development and resource creation for Mi’gmaw language education, beginning with the conjugation of one verb and will gradually expand to others. The project emphasizes ethical considerations, including informed consent and fair compensation for participants. Ultimately, this tool will contribute to the broader goal of revitalizing the Mi’gmaw language by providing educators with accessible resources for language instruction.
Martin, who started learning her language at the age of eight, has experienced the challenges of cultural disconnection herself and recognizes the transformative power of language in shaping one’s worldview. “Language was the most transformational in terms of changing my ways of thinking and seeing the world,” she says.
Call to all Canadians for support
Martin has been teaching her language to youth in her community since 2019. The ongoing support of her childhood language teachers helps her recreate the same kinds of rich relationships with her students today and is ensuring that future generations embrace their culture with pride. She says students often tell her they will also become teach in Mi’gmaw one day. “I believe it is now that we lay the seeds, the tools, and the love for our languages to ensure all of our languages survive,” she says.
Looking to the future, Martin is hopeful. “I see a bright future for Mi’gmaw immersion,” she says.
But Martin says that the responsibility for language revitalization extends beyond Indigenous communities. She calls upon all Canadians to recognize the value of linguistic diversity and to actively support efforts to preserve and promote Indigenous languages. “It will take every single Canadian to support and make space for languages on their territories,” she says.
“Wela’lieg Gisulgw ugjit tli’suti, ta’n telolti’gw, ugs’tqamu aqq ta’n teliangweiwi’eg ‘ms’t,” she says. “Thank you, Creator, for the language, our culture, the earth and taking care of us all.”
Challenging AI algorithms
Dane Malenfant, a citizen of Métis Nation Saskatchewan, is finishing the first year of a master’s degree in Computer Science.
Malenfant’s thesis focuses on the principles of reciprocity – a fundamental concept deeply ingrained in traditional Plains Indigenous ways of life. Malenfant is investigating whether this is a learnable concept in current artificial intelligence (AI) systems.
Reciprocity, as Malenfant explains, is more than just a notion. “It’s a way of being – a tradition rooted in giving and receiving, equilibrium with the natural processes of the world,” he says.
Drawing from his experiences growing up in Saskatchewan and understanding Métis and other Plains Indigenous histories and culture, Malenfant wants to challenge AI algorithms to understand and embed these teachings.
Reciprocity and cooperation
The Manitokanac which means “the great spirit,” are wooden shrines set up in areas, like travel routes, by Plains Indigenous peoples, offering tools, food, and medicine for travelers. These shrines are physical images of reciprocity, where one takes only what is needed and gives back what is no longer required – a practice traditionally emphasized in Métis and other Plains Indigenous values.
His research project challenges the current state-of-the-art AI systems by testing the concept of traditional reciprocity into reinforcement learning tasks as an extension of previous machine learning work on reciprocity and cooperation. Specifically, the project utilizes a novel redesign of the classic credit assignment problems, where AI agents must navigate and decide the importance of actions and places at different times. Future work will explore the use of more advanced decision-making algorithms and more complex environments to further investigate the emergence of traditional reciprocity behaviour in AI systems.
Through research like his, Malenfant envisions a collaborative future where traditional Indigenous science and knowledge are represented, and the development of AI will improve language acquisition and economic opportunities for Indigenous nations.