Placing Gitxsan stories in text : returning the feathers, Guuxs Mak’am mik’aax
Bibliographic record
Abstract
As a Gitxsan child growing up in the Gitxsan territory, I was never sent away to residential school. I was able to experience a traditional Gitxsan education that involved working with Elders on the land and listening to stories. This experience had a profound effect on my way of being, both as an educator and as a storyteller, so much so that I have used this pedagogical approach in my public school teaching. This study documents my journey as I concurrently use stories as research and research as stories and drawing from narrative, autobiographical, reflective practice, and action research literature and the conception of Indigenous research offered by Linda Smith (1999). I narrate the stories and legends that reveal the depth of the Gitxsan culture. Gitxsan culture involves traditions arising from a long oral history. I explore these traditions and stories and transform them into text so that they can be used as an educational resource in order to help students think critically and understand factual content in a personalized manner. Gitxsan educational materials can and should be integrated into the common school curriculum. Gitxsan perspectives on storytelling offer useful insights that would enhance education programs within our public school systems. This thesis/dissertation captures the diversity and complexity of the Gitxsan culture and explores some of the struggles and tensions associated with an inquiry into educational change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".