A Pedagogical Corpus to Support a Language Teaching Curriculum to Revitalize an Endangered Language
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An obstacle to revitalizing an endangered language is the shortage of authentic speech samples for learners to use as models. Digital recordings of community elders performing traditional chores and special rituals or narrating legends and myths are often made to overcome this obstacle. These recordings, however, contain speech that lacks the crucial features of conversational speech that make them appropriate instructional models. Effective model utterances should be short, have a stand-alone format, and have similar structures to utterances used in everyday transactions, which must be labeled and tagged and organized into a searchable corpus. To date, however, no such corpus exists for indigenous languages, and compiling one is an enormous task. To provide native speech models for adult Labrador Inuit learning their endangered language, Inuttitut, the authors explored the feasibility of building a specialized corpus potentially useful for aiding classroom instruction, using an internationally recognized open-source search and retrieval system called Topic Maps to create its database.
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.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it