At-Home Anishinaabemowin Language Immersion: Practices of Adult Learners
Bibliographic record
Abstract
In Indigenous language contexts where many adults are not fluent speakers, adult learners play a key role in the restoration of intergenerational transmission. Adult learner narratives of at-home immersion in particular offers an important window into Indigenous language revitalization. In this paper we, two adult Anishinaabemowin learners, discuss the practices of at-home immersion with a toddler in Ontario, Canada. Drawing from an interview and autoethnography, we investigate our teaching and learning activities of at-home immersion, as well as the conditions that facilitate our at-home immersion practice. Our immersion practice is a creative collaboration enacted through both planned and unstructured learning and teaching activities, and is facilitated by our specific circumstances of access to postsecondary education and flexible time. We argue these language practices reflect certain relationships to Anishinaabemowin and English, which informed our efforts to (re)assert the space of immersion. These practices also reflected an orientation to other speakers and learners that is grounded in an understanding of the fundamental importance of more relationship-building for intergenerational transmission. Thinking in terms of relationships is one way for us to identify priorities and opportunities toward creating long-term, sustainable at-home immersion.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".