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Record W2104311210 · doi:10.25071/1916-4467.17988

Unsettling Fictions: Disrupting Popular Discourses and Trickster Tales in Books for Children

2009· article· en· W2104311210 on OpenAlexaffvenue
Judy M. Iseke-Barnes

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsLakehead University
Fundersnot available
KeywordsTricksterIndigenousAppropriationStorytellingSociologyIndigenous educationPerspective (graphical)Face (sociological concept)Media studiesNarrativeAestheticsGender studiesAnthropologyVisual artsLiteratureSocial scienceEpistemologyArtPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.289
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2009
Admission routes2
Has abstractyes

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