Integrating collaboration into the classroom: Connecting community service learning to language documentation training
Bibliographic record
Abstract
As training in language documentation becomes part of the regular course offerings at many universities, there is a growing need to ensure that classroom discussions of documentary linguistic theory and best practices are balanced with the practical application of these skills and concepts. In this article, we consider Community Ser-vice Learning (CSL) in partnership with community-based organizations as one means of grounding language documentation training in realistic and collaborative practice. As a case study, we discuss one recent CSL project undertaken as a collaboration between the Yukon Native Language Centre and graduate students in a semester-long introductory course on language documentation at Carleton University. This collabo-ration focused on annotating recently digitized legacy language lessons for several Indigenous languages spoken in the Yukon Territory, Canada, using documentary linguistic software tools to create a text-searchable, multimedia database for future pedagogical applications. Drawing on the reflections of both community- and univer-sity-based collaborators, we discuss the design of this project, some of the challenges that needed to be addressed as it progressed, and offer several recommendations for future initiatives to integrate CSL into language documentation training.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".