Bibliographic record
Abstract
Canada’s Truth and Reconciliation Commission has sparked new discussions about reforming education to move beyond colonialist representations of history and to better reflect Indigenous worldviews in the classroom. Trickster Chases the Tale of Education considers the work of educators and Mi’kmaw community members, whose collaborative projects address the learning needs of Aboriginal people. Writing in the form of a trickster tale, Sylvia Moore contrasts Western logic and Indigenous wisdom by presenting dialogues between her own self-reflective voice and the voice of Crow, a central trickster character, in order to highlight the convergence of these two worldviews in teaching and learning. Exploring the challenges of incorporating Indigenous ways of knowing, doing, and being into education, this volume weaves together the voices of co-researchers, community members, and traditional Mi’kmaw story characters to creatively bring readers into the realm of Indigenous values. Through a detailed study of a community project to highlight the important connection between the Mi’kmaw and salmon, Moore reveals teachings of respect, reciprocity, and responsibility, and emphasizes the need for repairing and strengthening relationships with people and all other life. These dialogues demonstrate the need for educators to critically examine their assumptions about the world, decolonize their thinking, and embrace Indigenous knowledge as an essential part of curriculum. Using the power of storytelling, dreams, trickster figures and their teachings, humour, and contemplative silences, Trickster Chases the Tale of Education will resonate while providing insights into Indigenous learning and teaching.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.047 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".