Developing an On-line Cree Read-along with Syllabics. Technical Report 2006-01
Bibliographic record
Abstract
East Cree is a Native American language spoken on the Eastern coast of James Bay, Quebec, \nCanada. Like many other Aboriginal languages, it is struggling to survive. Using participatory action \nresearch (Morris & Muzychka, 2002; Junker, 2002), the eastcree.org project (www.eastcree.org) is \nexploring how Information Technology can assist language documentation, preservation and \ntransmission. We report here on the development of on-line read-along material, whose goal is to \nstrengthen literacy in Cree syllabics. Since Cree became the language of instruction in all Cree \nschools in 1995, the department of Cree Programs (the curriculum development unit of the Cree \nSchool Board for Cree language and culture) published hundreds of books in Cree syllabics \n(Burnaby et al. 1999a, 1999b). We were asked to explore possibilities of adapting such books to the \nweb in order to have the story read back to the user and also to teach correct spelling by highlighting \nthe portions of text on each page, as it is being read.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.022 |
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".