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Record W4286291881 · doi:10.3138/jeunesse-14.1.01

Healing Intergenerational Trauma through Cultural Reclamation in David Alexander Robertson’s Cree-Centric Retelling of <i>The Lion, the Witch and the Wardrobe</i>

2022· article· en· W4286291881 on OpenAlexvenueno aff
Petra Fachinger

Bibliographic record

VenueJeunesse Young People Texts Cultures · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWitchIndigenousNarrativeHistoryAnthropologyGender studiesEthnologySociologyLiteratureArtEcology

Abstract

fetched live from OpenAlex

In this article, I argue that Cree author David Alexander Robertson’s YA novel The Barren Grounds retells C.S. Lewis’s war trauma narrative The Lion, The Witch and the Wardrobe from a Cree perspective. The “war” addressed in The Barren Grounds is that of the violent acts of colonization that have disconnected several Indigenous generations from their ancestral cultures. The compulsion to reimagine this British classic story in a way that focuses on his own cultural background shows that there was something missing for Robertson in the source text: his Cree identity. Using as a framework Suzanne Methot’s approach to complex post-traumatic stress disorder (CPTSD), which results from repeated traumatic experiences over a prolonged period, I demonstrate that The Barren Grounds emphasizes the significance of cultural reclamation for the healing of intergenerational trauma, including trauma resulting from the foster care experience. The Indigenization of Lewis’s story recognizes children’s rights to an education that includes Indigenous children’s and YA literature and adopts nation-specific Indigenous knowledge as a framework for reading this literature.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0150.039
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJeunesse Young People Texts CulturesSame topicThemes in Literature AnalysisFrench-language works237,207