Developing Intermediate Language Learning Materials A Labrador Inuttitut Story Database
Bibliographic record
Abstract
This paper 1 describes the collaboration between two linguists and a public school language teacher in the making of a story database for use in the second language learning of Labrador Inuttitut in Canada. First, we describe the process through which the collaboration took place. Linguists who are working with communities have linguistic goals, and communities have long-term language teaching goals. Where the two goals intersect, it is possible to have mutually useful collaboration. One of the challenges is to determine whether or not there is indeed intersection of goals so that precious time and effort is not wasted. Next, we describe the development of a story database that has the properties that we believe are optimal for intermediate language learners. It will have a large amount of original Inuktitut data and will also have extra information for learners that is hidden from view unless the learner chooses to look at it. We believe that Internet story publishing is faster, cheaper and can reach a larger audience than traditional publishing. It
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".