Across mountain and sea: Bringing language to linguists
Bibliographic record
Abstract
We report on a project bringing Tlingit language speakers to the University of British Columbia (UBC) as resident consultants for one academic year. Spearheaded by community members, this project contributes to a model of âbest practicesâ (Penfield et al. 2008) for community-based language research.\n Our goal was to establish a sustainable long-term collaboration by building community and scholarly capacity for language revitalization, research, and training. Our successful integration of theory, practice, and application included:\n \n (i) New relationships with fluent speakers from two Tlingit speech communities (Coastal and Inland). This is a breakthrough, as modern nation-state boundaries between Alaska (USA) and the Yukon Territory (Canada) have obscured traditional Coastal-Inland relations. To date, most linguistic research has focused on Coastal Tlingit, so having access to both varieties allows us to document previously un-described differences.\n (ii) Student training for future work on the language: a field methods course parallels the research project, allowing us to train several undergraduate and graduate students, a subset of which continue to work on the language.\n (iii) Collaboration between linguistic subfields: leveraging expertise in different subfields (phonology, syntax, semantics, linguistic ethnography, and language pedagogy) affords a breadth of scope that would otherwise be impossible.\n (iv) Inter-institutional and international collaboration: the project involves two research institutions (UBC and University of Alaska Southeast) in two different countries (Canada, USA). This is especially important in Canada, as no Canadian-based research is currently conducted on Inland Tlingit.\n (v) Outreach within the university community: the project features in the Language of the Year initiative undertaken by UBC Linguistics, which brings to the attention of the university community the contribution that field-based linguistic research makes to language stabilization and revitalization.\n \n Bringing speakers to linguists â what we call the Convergent Streams model â used to be very common, but is no longer widely practiced. However, sustained involvement of linguists with different types of expertise over an extended period of time permits rapid advance across several domains and maximizes the impact of scholarly research on language teaching efforts. Our experience suggests that Convergent Streams is most effective when hosts involve administrators early, arrange culturally appropriate compensation such as honoraria rather than salaries, organize transport and accommodation, monitor wellness needs and ensure timely access to medical services, coordinate socio-cultural support by offering meaningful companionship, and have a plan for return to the community.\n \n Penfield, Susan D., et al. (2008) Community collaborations: best practices for North American indigenous language documentation. International Journal of the Sociology of Language 191, 187-202.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".