We Learn Together: Fostering a Love for Anishinaabemowin in the Hearts and Classrooms of Urban Indigenous and Non-Indigenous Students and Teachers
Notice bibliographique
Résumé
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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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,025 | 0,011 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,015 |
| Intégrité de la recherche | 0,001 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».