Reconciling difference and building trust: International collaboration in indigenous language revitalization
Bibliographic record
Abstract
In the summer of 2008, the University of California at Santa Barbara hosted a six-week "InField" institute (www.linguistics.ucsb.edu/faculty/infield/) that brought together Indigenous language activists and linguistic scholars to share experience and expertise on issues related to endangered language documentations. The first two weeks featured a concentrated series of workshops, offering instruction in field methods, video, audio, life in the field, grant writing, documentation software, language activism, and an introduction to linguistics. Each day there was a Language Revitalization Model presented by endangered language activists and/or linguistic scholars. The remaining four weeks of the program focused on applying these skills to intensive documentation-for-revitalization of 3 very different endangered languages, one from Kenya, one from Sierra Leone, and one from British Columbia: Kwak’wala. Under the sponsorship of a SSHRC Strategic Aboriginal Research project on Kwak’wala, two fluent Elders and four younger Kwakwaka'wakw community members participated in this program, along with a very diverse group of other learners - Aboriginal and non-Aboriginal, from Canada, across the US, and Europe. The challenges of this context - a wide range of individual talents and expertise, academic skills, cultural backgrounds, short- and long-term goals, institutional expectations, personal apprehensions, experience with or ignorance of historical appropriation issues - were brought together by a shared dedication to this language revitalization initiative. Under the broad rubric of “Indigenous – Academic Relationships” we invite you to hear of our experience working collaboratively within the Aboriginal and academic communities as we explore the issues confronting the diverse constituencies. We will discuss measures of success along with residual challenges, and will share strategies used to address issues of difference that frequently interface with language revitalization initiatives, such as trust, race, expertise, entitlement, and intellectual property rights. We aim to impart the value we have found in recognizing, reconsidering and confronting traditionally divisive issues as we pursue the on-going challenges nurturing Indigenous language survival
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.032 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".