Designing Corpus-Creation Tools for Language Revitalization
Bibliographic record
Abstract
ABSTRACT: We have developed a set of corpus-creation tools to support the documentation and revitalization of Isga I?abi (also known as Stoney ), a Siouan language spoken in Alberta. This project has emerged from a collaboration between community language champions and university-based researchers, with the goal of creating a new generation of Stoney speakers. The initial phase of the project has focused on expanding the documentary record of the language by creating a corpus of spoken Isga I?abi, recorded from nearly a dozen fluent speakers. We describe the particular constraints that informed the design of the project and how they led us to create several new tools for elicitation. First was an adaptation of the Summer Institute of Linguistics' Rapid Words Collection method, where, instead of focusing on individual lexemes, we collected thematically organized sentences displaying targeted grammatical properties. Next, we developed a photo prompter tool, which allows speakers to describe what they see in a photo, but also to discuss the photo with other speakers in spontaneous discourse. These simple tools allow the speakers to handle the day-to-day work of language documentation themselves, without needing a linguist to be present during those sessions. The outputs from this process (currently over fifty hours of audio) will find their way into various resources and activities for language teachers and learners. Insights from the Isga I?abi speakers themselves reflect on their use of the tools and their perspectives on the project to date.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".