A Fairy-Tale Noir: Rewriting Fairy Tales into Feminist Narratives of Exposure
Bibliographic record
Abstract
This article introduces the fairy-tale noir, a subgenre of fantasy-noir fiction that is particularly present in the work of Italian women writers, including Laura Pugno, Simona Vinci, Nicoletta Vallorani, and Alda Teodorani. This subgenre adopts fairy-tale topoi and characters to elaborate on the theme of vulnerability from feminist and environmental perspectives. Vulnerability is an intrinsic feature of fairy tales (texts that are continually performed and modified, but that remain “non-appropriable”); it is also a pivotal characteristic of the young protagonists of these fictional universes, who are often exposed to abuse. The twenty-first-century fairy-tale noir redeploys the discourse of bodily exposure typical of traditional fairy tales by engaging in an environmentalist reflection on the experience of exposure that human and nonhuman bodies share. The genre also adopts the theme of vulnerability as openness to change and uses the unconventional families of fairy tales to discuss recent social changes in Italian families. Finally, fantasy noir recasts vulnerability to violence as a potential space of empathy, or biophilia, with the broader, nonhuman “family.” Exploring this overlooked genre ultimately shows how Italian women writers, who are still at the margins of the Nuovo Giallo Italiano, have successfully reinvented a male-dominated genre into a literary lens probing socio-environmental concerns, first and foremost gender discriminations.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".