Community-Anchored Assessment of Indigenous Second Language Learning in K-12 Schools
Bibliographic record
Abstract
Indigenous second language programs in K-12 schools contribute to culturally nourishing education and to the revitalization of Indigenous languages. Assessing Indigenous second language learning presents particular opportunities and challenges based on the linguistic, historical, political, cultural, and social contexts in and for which the Indigenous language is being taught and learned. The self-governing Inuit region of Nunatsiavut is concerned with developing effective and appropriate tools for assessing students’ Inuttitut in order to evaluate how well K-12 programs are working so far, identify the basis on which future K-12 Inuttitut curriculum may be developed, and support ongoing assessment of learning and for learning in Inuttitut classrooms. This article discusses ways in which Inuit teachers in Nunatsiavut and a curriculum evaluation team have developed and implemented assessment tools and practices to evaluate Inuttitut learning in Nunatsiavut area K-12 schools. We discuss how Indigenous language learning and assessment, even when it occurs as part of an official school program, can be anchored in families and community. Families and communities need to be part of establishing language learning goals. Inuit teachers are drawing in full community resources and building a community of practice including Elders, other language speakers, leaders, principals, and teachers, to support and create contexts for community-anchored Inuttitut learning and assessment.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".