My Plains Cree (nêhiyawêwin) language classes
Bibliographic record
Abstract
I would like to share some of the experiences I have had as a Cree speaker and language instructor who is now becoming a Cree linguist. CILLDIâs Community Linguist Certificate program has provided me with much insight about my language. As a fluent speaker who also studied Cree at the university for 4 years, I was not aware of the importance of morphological structure, meaning, and sentence patterns of nÈhiyawÈwin. The Cree courses I took focused on formal grammar and the rules and functions of nouns, verbs, and sentences in isolation. With the CLC courses, I began to think deeply about how rich nÈhiyawÈwin is and started to understand the complexities of the language and how everything is interconnected. In the CLC classes, we examined word formation patterns and explored ways to coin new words without losing the trail of meaning. For example, in Cree the word for âgreenâ is askihtakwâw and is connected to askiy, the word for âmother earthâ. In English, what is the word âgreenâ connected to? I believe that in learning Cree, one needs to make these cultural and environmental associations. The CLC has helped me better understand the relationship between Cree words and Cree language and culture.\n \n In this paper, I describe how the CLC has affected my teaching of Cree at the University of Alberta. I will use the example of an intensive Introduction to Cree class in an immersion setting. This is a 7-day, all-day course for adult beginners offered through CILLDI. I use story-telling (both traditional Cree stories and familiar English fairytales) as the starting point for lessons on pronunciation, word structure, and word order in sentences. By basing my lessons around stories, I can use a lot of visual props and repetition, which help my students understand what Iâm saying. With stories and simple commands and questions based on the expressions in the stories, I can keep my utterances whole and constantly contextualized. nÈhiyawÈwin remains the focus, not a set of lessons based on greetings, lists of object names, or TPR-commands that wouldnât be very appropriate with adult learners. After working through individual stories, I give my students a written and glossed version of each story to reinforce the utterances that they have heard. I do not arrange the lessons from simple to more complex words and sentences, but take the language as it comes in the stories and build what I do in the classroom from there.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.013 |
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; both teacher heads agree on what is shown here.
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".