We Are Our Language: An Ethnography of Language Revitalization in a Northern Athabaskan Community
Bibliographic record
Abstract
For many communities around the world, the revitalization or at least the preservation of an indigenous language is a pressing concern. Understanding the issue involves far more than compiling simple usage statistics or documenting the grammar of a tongue--it requires examining the social practices and philosophies that affect indigenous language survival. In presenting the case of Kaska, an endangered language in an Athabascan community in the Yukon, Barbra Meek asserts that language revitalization requires more than just linguistic rehabilitation; it demands a social transformation. The process must mend rips and tears in the social fabric of the language community that result from an enduring colonial history focused on termination. These disjunctures include government policies conflicting with community goals, widely varying teaching methods and generational viewpoints, and even clashing ideologies within the language community. This book provides a detailed investigation of language revitalization based on more than two years of active participation in local language renewal efforts. Each chapter focuses on a different dimension, such as spelling and expertise, conversation and social status, family practices, and bureaucratic involvement in local language choices. Each situation illustrates the balance between the desire for linguistic continuity and the reality of disruption. We Are Our Language reveals the subtle ways in which different conceptions and practices--historical, material, and interactional--can variably affect the state of an indigenous language, and it offers a critical step toward redefining success and achieving revitalization.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".