Qallunaat Learners of Inuktitut: A Qualitative Investigation of How and Why Non-Inuit Learn Inuktitut as a Second Language
Bibliographic record
Abstract
Qallunaat, the Inuktitut term for people of non-Inuit heritage, is one demographic which is often not mentioned when discussing Inuktitut language revitalisation.Yet there is an increasing number of Qallunaat moving to Inuit Nunangat, the traditional Inuit homeland, who do not learn Inuktitut or another Inuit language.The present study investigates the motivations and attitudes of Qallunaat who learn Inuktitut.Using the L2 Motivational Self System (Dörnyei, 2005) as a theoretical framework, the present study considers the internal and external pressures for why a Qallunaat might choose to learn Inuktitut.The data, collected from interviews with Qallunaat learners of Inuktitut (n=7), suggest that this demographic is motivated primarily by internal factors.Prominent motivations for learning Inuktitut included interest in Inuit culture, enjoying the challenge of learning this language and a desire to communicate with Inuit.The present study also investigates which language learning resources Qallunaat have access to and which resources they believe are currently missing.Having access to a proficient speaker of Inuktitut was the highest rated resource for these learners.Among the resources deemed missing, the most prominent request was for learning materials aimed at advanced learners of Inuktitut.A number of pedagogical and theoretical implications are herein discussed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".