Bibliographic record
Abstract
Abstract The Ojibwe language, also referred to as Anishinaabemowin, is the language of the Ojibwe people in the Great Lakes region of North America. It has many mutually intelligible dialects and variations, making it one of the largest Indigenous languages in North America. While Ojibwe is an endangered language, with most speakers in the United States over the age of 70, it is also one that is being revitalized. In Minnesota and Wisconsin, the Ojibwe language is very widely taught and supported in both formal and informal educational contexts. It is taught in many preschools, elementary schools, and secondary schools and in tribal colleges and universities. Outside of institutions, families and individuals have made great strides to reclaim Ojibwe as their home language. Language camps, family language gatherings, and language tables are popular and can be found throughout the year. One of the most outstanding examples of reclamation is the Waadookodaading Ojibwe Language Immersion Institute in northern Wisconsin. Waadookodaading impacts the entire area’s Ojibwe language-learning communities by showing that an immersion school can indeed produce highly proficient second-language speakers. Immersion schools, preschools, and family language camps are numerous throughout the midwestern United States and Canada, and many families now trying to use Ojibwe as their home language. However, the economic hurdle remains; that is, jobs that demand Ojibwe language as a daily useful skill are sparse. Although there are many institutions that teach Ojibwe as a subject, this teaching can sometimes only be a doorway to language appreciation rather than fluency. Despite these challenges, the resilient spirit of individuals connecting language and identity loss directly to the colonization of Ojibwe and other Indigenous people is a fierce one.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".