Bibliographic record
Abstract
My interest in the Master of Arts program is to study the impact that the WilpWilxo'oskwhl Nisga'a has made on the Nisga'a Nation by providing post-secondary education.This thesis focuses on the educational journeys of four students while attending the Wilp Wilxo'oskwhl Nisga'a Institute and how they were able to succeed in obtaining their Bachelor of Arts degrees within their own traditional territory.Stories of success are important to hear, so that others can see education is not achieved overnight, but is a series of small steps taken every day.These educational journeys are metaphorically equated to a journey along the K'alii Aksim Lisims River that runs through the territorial lands of the Nisga'a.There are four classifications of action that organize my presentation of each student's journey in education: 1) Taking the Helm, Exploring Options 2) Life Currents of the Student 3) Learning to Paddle 4) Coming Ashore.Based on the Lisims (Nass River) these themes emphasize how life experiences involve different water currents and depths, runoffs, reflections, and routes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".