Unsettling Fictions: Disrupting Popular Discourses and Trickster Tales in Books for Children
Bibliographic record
Abstract
This paper examines why stories and storytelling within education are important, discusses reasons why appropriation of stories is problematic, raises issues with the process of sharing cultural stories from around the world, and discusses Trickster stories and the complexity of these stories. With this background, we then critique the book Raven: A Trickster Tale from the Pacific Northwest by Gerald McDermott as an example of the complexity teachers, parents, and librarians face in teaching with books for children that are appropriated from Indigenous knowledges. In an example of teachers engaging with a story told from an Indigenous perspective, A Coyote Columbus Story (King, 1992), students engage in disrupting boundaries and transforming the classroom experiences. These examples highlight the challenges and responsibilities of rejecting literature that appropriates Indigenous knowledges and moves towards teaching with Indigenous literatures. Educators are challenged to consider Indigenous literatures written from Indigenous perspectives and to engage with these in ways that transform educational experiences.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.035 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".