MétaCan
Menu
Back to cohort
Record W4391066308 · doi:10.5539/ells.v14n1p9

“New Wine in Old Bottles”: Ethical Literary Criticism, Adaptations and Angela Carter’s Little Red Riding Hood

2024· article· en· W4391066308 on OpenAlexvenueno aff
Anqi Peng, Xi Chen

Bibliographic record

VenueEnglish Language and Literature Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyContext (archaeology)Order (exchange)History

Abstract

fetched live from OpenAlex

This paper reexamines the classic fairy tale “Little Red Riding Hood” and, in conjunction with Angela Carter's adaptations, “The Werewolf”, “The Company of Wolves”, and “Wolf-Alice”, reimagining and analyzing the story from the perspective of literary ethics. Both the original version of “Little Red Riding Hood” and Carter’s adaptations exhibit strong ethical consciousness. Carter’s artistic creation is deeply influenced by European traditional fairy tales in many aspects. The tragic endings of “Little Red Riding Hood” and Carter’s adapted version, “The Company of Wolves”, symbolize the disruption of ethical order. In both versions, the breakdown of ethical order is a result of violating ethical taboos, usurping ethical identities, and allowing free will to dominate, disregarding rational will. The conclusions of these two ethical tragedies serve as a warning to people: any violation of ethical taboos, trespassing on identity, and disruption of ethical order will bring punishment. Meanwhile, Carter’s adapted versions also illustrate the artistic charm and value of tragedy, as well as the practical significance of upholding ethical order. The paper analyzes key themes such as gender, power, morality, and self-awareness in the adaptation and explores how these themes can be reinterpreted through literature, placing the story in a contemporary context to enhance its relevance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.017
GPT teacher head0.274
Teacher spread0.258 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language and Literature StudiesSame topicFolklore, Mythology, and Literature StudiesFrench-language works237,207