Bibliographic record
Abstract
As a member of the Tahltan Nation, I carried out research that centred on community experiences of language reclamation. The investigation focused on how language reclamation is connected to health and healing, as well as what has been done and what still needs to be done to revitalize and reclaim the Tahltan language. Language reclamation is the start of a process in which our people heal from the impacts of colonization and assimilation by reclaiming our language, culture, and identity, thereby allowing our voices to become stronger and healthier. From what was learned from community co-researchers, scholars who have worked with our communities, Indigenous community language revitalization experts, and international language revitalization scholars, I developed a Tāłtān Language Reclamation Framework focusing on governance; language programming; documentation; training and professional development; and resiliency, healing, and well-being. This report will discuss the ways in which this framework has been implemented in community over the last decade, highlighting examples such as the formation of a language governing body, Dah Dẕāhge Nodeside (Tahltan Language Reclamation Council); the implementation of language nests; the development of a Tāłtān language school K–8 curriculum; the creation of learning materials based on old and new recordings of first language speakers (e.g., digital apps and videos, websites, alphabet book, grammar resources); post-secondary fluency/proficiency community programming; and documentation training. Finally, we continue to focus on the relationship between language reclamation, intergenerational trauma, and healing, resiliency, and well-being. This will be done through community-based immersive programming that focuses on the nurturing of relationships with first language speakers in order to create not only learning resources, but safe and supportive environments for all speakersーlearners, second language speakers, silent speakers, and first language speakers.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".