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Record W4416607889 · doi:10.5539/ass.v21n6p70

Narrative Therapy and Toni Morrison’s Beloved: Healing Through Storytelling

2025· article· W4416607889 on OpenAlexvenueno aff
Zhongqiang Wang

Bibliographic record

VenueAsian Social Science · 2025
Typearticle
Language
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeStorytellingEmbodied cognitionPerspective (graphical)Narrative inquiryNarrative criticismConstruct (python library)Bridging (networking)

Abstract

fetched live from OpenAlex

This paper explores Toni Morrison’s Beloved through the theoretical framework of narrative therapy. Narrative therapy proposes that individuals are not defined by their problems but are shaped by the narratives they construct within cultural and relational contexts. Applying this perspective to Beloved, the paper examines how Sethe, the protagonist, becomes trapped in a trauma-saturated story rooted in the horrors of slavery and personal loss. The analysis highlights key narrative therapy concepts—such as externalization, double listening, and re-authoring—demonstrating how Morrison dramatizes these processes through the externalized figure of Beloved, the role of community intervention, and the emergence of alternative narratives embodied by Sethe’s daughter, Denver. Ultimately, Morrison depicts healing not as the elimination of trauma, but as a continuous, collective process of rewriting one’s life narrative. By bridging literary analysis and psychological theory, this paper illuminates how Beloved serves as both a literary masterpiece and a powerful reflection on narrative identity, resilience, and the human capacity for renewal.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.348
Teacher spread0.321 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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