Bibliographic record
Abstract
When two of my grandmothers were dying, they reverted to communicating in their traditional language. There was no one at their sides who could understand their dying words. This is one of the reasons I choose to learn my language and examine at a more holistic way of language revitalization. While community learners attend language classes, use master-apprentice techniques and study language resources, we are running out of time to save our critically endangered language isolate. The handful of fluent teachers are over 80 years of age and the Haida communities on Haida Gwaii, BC in Canada and in southeast Alaska are in the race of our lifetime to ensure our language survives. We need to slow down, offer a prayer and call upon our ancient spirituality and beliefs. Haida ancestors once used ceremonies, prayers and medicines to empower their speech, songs, memories and place in this world. They called upon the spirits of Story-woman, Lady Luck and others for help. There is a great need amongst the Haida and other indigenous peoples to have a deeper understanding of indigenous epistemology, tradition and spirituality to ensure our languages survive. Through archival research, elder interviews and personal practice, I will share Haida epistemology as well as the ancient traditions that can hep to revitalize our dying language.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.014 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".