Integrating Indigenous voices: enhancing cultural competency through children's literature
Bibliographic record
Abstract
This project-based thesis examines how Indigenous children's literature, primarily through my picture book Celebrating Potlatches, can enhance cultural understanding among students in grades 1 through 5, ages 6 to 11, in British Columbia. This project aligns closely with BC's English Language Arts and Social Studies curriculum. As outlined by the BC Ministry of Education and Child Care, students are expected to "explore stories and other texts to help us understand ourselves and make connections to others and the world" (BC Ministry of Education and Child Care, n.d., para. 16). This integration of storytelling and First Peoples' perspectives provides a framework for developing empathy and respect for Indigenous cultures. Celebrating Potlatches is crucial, as this picture book was created using authentic Indigenous voices and traditions to bridge cultural gaps through storytelling. My research highlights the historical discrimination faced by Indigenous students within the Canadian education system and underscores the urgent need for educators to develop greater cultural competency. By integrating Indigenous children's books like Celebrating Potlatches, educators can address these issues and provide a platform for sharing Indigenous history and values. The book emphasizes the significance of Potlatch traditional ceremonies practiced by Northwest Coast Nations as a form of cultural medicine that strengthens community bonds and preserves identity. This educational approach challenges stereotypes and deepens understanding of Indigenous traditions, fostering inclusive and respectful learning environments. Incorporating Indigenous stories into education is crucial for enhancing cultural competency and advancing Reconciliation efforts in Canadian schools.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".