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聽Mi鈥檊maw language
Karen Martin, a Mi鈥檊maw student from Listuguj, is in her second year of a master鈥檚 degree in the program of Education and Society. Martin鈥檚 thesis aims to create a Mi鈥檊maw Verb Conjugation Tool to support language revitalization efforts in Listuguj. 聽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鈥檚 project will establish a database format reference tool that can aid in curriculum development and resource creation for Mi鈥檊maw 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鈥檊maw 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鈥檚 worldview. 鈥淟anguage was the most transformational in terms of changing my ways of thinking and seeing the world,鈥� 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.聽She says students often tell her they will also become teach in Mi鈥檊maw one day. 聽鈥淚 believe it is now that we lay the seeds, the tools, and the love for our languages to ensure all of our languages survive,鈥� she says.
Looking to the future, Martin is hopeful. 聽鈥淚 see a bright future for Mi鈥檊maw immersion,鈥� 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. 鈥淚t will take every single Canadian to support and make space for languages on their territories,鈥� she says.
鈥淲ela鈥檒ieg Gisulgw ugjit tli鈥檚uti, ta鈥檔 telolti鈥檊w, ugs鈥檛qamu aqq ta鈥檔 teliangweiwi鈥檈g 鈥榤s鈥檛,鈥� she says. 鈥淭hank you, Creator, for the language, our culture, the earth and taking care of us all.鈥�
Challenging AI algorithms
Dane Malenfant,聽a聽citizen of M茅tis Nation Saskatchewan, is finishing the first year of a聽master鈥檚 degree in Computer Science.
Malenfant鈥檚 thesis focuses on the principles of reciprocity 鈥� a fundamental concept deeply ingrained聽in聽traditional聽Plains Indigenous聽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. 鈥淚t鈥檚 a way of being 鈥� a tradition rooted in giving and receiving,聽equilibrium聽with the natural聽processes of the聽world,鈥� he says.
Drawing from his experiences growing up in Saskatchewan and聽understanding聽M茅tis and other聽Plains Indigenous聽histories and culture, Malenfant wants to challenge AI algorithms to understand and聽embed聽these teachings.
Reciprocity and cooperation
The Manitokanac which means 鈥渢he great spirit,鈥� are wooden shrines set up聽in areas, like travel routes, by聽Plains Indigenous peoples, offering tools, food, and medicine for travelers. These shrines are聽physical images of聽reciprocity, where one takes only what is needed and gives back what is no longer required 鈥� a practice聽traditionally emphasized in M茅tis and other聽Plains Indigenous聽values.
His research project challenges the current state-of-the-art AI systems by聽testing聽the concept of聽traditional聽reciprocity into reinforcement learning tasks聽as an extension of previous machine learning work on reciprocity and cooperation. Specifically, the project utilizes a novel聽redesign of the聽classic credit assignment problems, where AI agents聽must navigate and decide the importance of actions and places at different times. Future work will explore the use of聽more advanced decision-making algorithms聽and more complex environments to further investigate the emergence of聽traditional聽reciprocity聽behaviour聽in AI systems.
Through research like his, Malenfant envisions a聽collaborative聽future where聽traditional Indigenous science and knowledge are represented,聽and聽the development of AI will improve language acquisition and economic opportunities for Indigenous nations.
