Assessing <scp>North American</scp> Indigenous Languages
Bibliographic record
Abstract
This chapter is an introduction to current practices in assessing North American indigenous languages. The languages of the indigenous people of North America are remarkably diverse. As a result of European colonization and subsequent political and educational policies, only about 200 mutually unintelligible languages remain, with the number of speakers of each ranging from less than 30 to more than 100,000. The chapter provides an introduction to some of the properties of these languages, and to their current patterns of use, as well as to their teaching and learning contexts. The chapter provides a brief overview of assessment practices for the following languages: Anihshininiimowin (northern Ontario), Inuttitut (northern Quebec), Seneca (New York state), and Cree (focusing on materials used in Alberta). Local decision making is very important for assessment of the language abilities that are valued and viable to achieve. Local decision making is also necessitated by the limitations of published information about these languages, by the dynamic and variable patterns of language use, and by the diverse types of teaching and learning contexts. Local educators, test developers, fluent speakers, and elders work collaboratively to assess the desired types of language use, especially because language is so closely intertwined with identity and culture.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".