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Record W4405613459 · doi:10.3138/cras-2024-001

Religion and Neuroscience in <i>Uncle Tom’s Cabin</i>

2024· article· en· W4405613459 on OpenAlexvenueno aff
Geneva Cobb Moore

Bibliographic record

VenueCanadian Review of American Studies · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)AmygdalaPsychoanalysisPerspective (graphical)ArtLiteratureTriad (sociology)NeuroscientistPhilosophyPsychologyNeuroscienceVisual arts

Abstract

fetched live from OpenAlex

“Religion and Neuroscience” invites a new reading of Stowe’s masterpiece from the perspective of these two combined subjects, depicting Eliza’s bodily stamina and Uncle Tom’s spiritual mindfulness over slavery’s evils. In neuroscience, the amygdala plays a key role in their confrontation with slavery: the flight/fight/freeze triad of stress hormones running throughout the body under duress is what causes Eliza to flee and Uncle Tom to freeze. Stowe chooses this pattern of flight for Eliza and freezing for Uncle Tom; their actions affirm her positive ideas about motherhood and Christian salvation. Images of neuroscience are well ahead of Stowe’s time with her description of the limbic system, housing the almond-shaped amygdala, but not identified by a neuroscientist until 1952. In multiple ways, Stowe’s novel advanced ideas that caught up with her a century later, although resonating emotionally with her nineteenth-century audience that made Uncle Tom’s Cabin a masterpiece against human bondage.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.013
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.323
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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