We Learn Together: Fostering a Love for Anishinaabemowin in the Hearts and Classrooms of Urban Indigenous and Non-Indigenous Students and Teachers
Bibliographic record
Abstract
Indigenous languages across Turtle Island, now known as North America, are in danger of becoming extinct. Gallery (2016) states that currently, only 60 of the previous 300 Indigenous languages that existed are being spoken. McIvor (1998) explains that language loss does not have to be a personal experience for one to feel the effects of it and that in fact language loss can be passed down through generations. The dire state of Indigenous languages across Canada is a direct result of historical and current laws and policies designed to assimilate Indigenous people’s languages to future generations, resulting in a significant decline of languages over time. Indigenous communities are working to save their languages through various methods including, but not limited to, immersion programming, language nests, and language programming at all levels of educational institutions. With this work in mind, the United Nations declared 2022-2023 the International Decade of Indigenous Languages, highlighting the need to preserve the Indigenous languages that are disappearing at an alarming rate (UNESCO, 2022). The purpose of this study is to examine the impact of an Anishinaabemowin pilot (language programming) in local schools on urban Indigenous youth and to observe how access to language programming in schools can contribute to Indigenous language revitalization efforts and cultural pride and confidence in Indigenous learners. The research was informed by four research questions: 1.) How does the language program benefit both Indigenous and non-Indigenous students? 2.) How can naturalizing Indigenous knowledge deconstruct the Eurocentric belief of ‘what is curriculum’? 3.) How can Indigenous and non-Indigenous community members support further language learning in our communities to create more fluent speakers? 4.) In what ways can we encourage teachers to continue with language revitalization work and teaching Indigenous content when the pilot is over? This study was conducted using a qualitative approach to research and integrated both Eurocentric qualitative research methods as well as Indigenous research methods which included both a community-based research approach and use of the conversational method. Data was collected through an Indigenous approach of a talking circle with community partners who were responsible for the creation and implementation of the Anishinaabemowin pilot. Data analysis indicated that there is a need for Indigenous language programming to continue at a school-based level and a desire from the Indigenous community to see educators continue to integrate Indigenous ways of knowing, being, and doing as well as Indigenous languages into their classroom communities. Findings of this study will be of interest to Ontario school boards and Indigenous communities looking to implement language programming in local schools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".