ONLINE LANGUAGE GAMES FOR ENDANGERED LANGUAGES (JEUX.TSHAKAPESH.CA & WWW.EASTCREE.ORG/LESSONS)
Bibliographic record
Abstract
It is often a struggle to create a strong presence on the web for Aboriginal languages and to make use of Information and Communication Technologies to support language preservation and maintenance. One crucial aspect of Aboriginal language retention, at least in Canada, has been the development of literacy in Aboriginal languages. We report here on a series of projects with two Aboriginal linguistic groups in Canada: East Cree and Innu. For the past six years, using a collaborative (participatory action) research framework with partners involved in language teaching, we have been developing online language lessons and games aimed at bilingual Aboriginal speakers (Cree-English and Innu-French) who wish to become literate in their language. The first set of lessons and exercises, developed in 2006, was aimed at fluent adult speakers of East Cree who had been educated in English and wanted to learn basic syllabic orthography. The subsequent sets had to take into consideration multiple uses and users, including a parallel development for the Innu language, which does not use syllabics. The latter sets of games include vocabulary enrichment, the teaching of grammatical concepts, and the discovery of language structure. Different interfaces allow for the ongoing creation of new lessons and exercises. Features include:
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.279 | 0.040 |
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".