O’nónna: A Curriculum for Land-Based Language Learning
Bibliographic record
Abstract
The Atlas of Kanyen’kehá:ka Space project (Kanyen’kehá:ka Nation, 2020; see www.mohawkatlas.org) launched in 2019 for the purposes of preserving Kanyen’kéha (Mohawk language) place names and related landscape terminology. Built using Nunaliit, a community mapping framework developed by Carleton University’s Geomatics and Cartographic Research Centre (GCRC), the Atlas is capable of pinning points onto a map and attaching media such as pronunciations, photos, videos and documents to that point. With funding for the Atlas through the Social Sciences and Humanities Research Council, the Atlas Research Team held workshops in Kanyen’kehá:ka communities to document their space with over 198 places and approximately 188 media files added from within the communities. However, we knew there was also potential to use the Atlas for language revitalization activities. With funding from the National Indian Brotherhood, we created a language learning curriculum integrating the Atlas and drone technology with land-based activities which was used during Land Back Summer Camp, held at the Native North American Travelling College in Akwesasne in July 2022. This paper discusses the components of the curriculum centred around the Atlas as task-based language learning. Because of the wide range of ages and language proficiency, the curriculum is a combination of different methods and strategies that are designed to reinforce each other. These included teaching the campers how to fly drones and integrate the footage into the Atlas of Kanyen’kehá:ka Space working with four L1 Kanyen’kéha speakers to describe the footage. The camp also included land-based activities as well as games and activities in Kanyen’kéha.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".