“New Wine in Old Bottles”: Ethical Literary Criticism, Adaptations and Angela Carter’s Little Red Riding Hood
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".