Laurie Halse Andersen’s <i>speak: the graphic novel</i> : creating and resisting aesthetic distancing to discuss the trauma of sexual violence
Bibliographic record
Abstract
In 2018, Laurie Halse Andersen’s groundbreaking novel Speak (1999) was adapted into a graphic novel, a form that effectively combines visual and verbal storytelling techniques. In stories about sexual assault, it is common for authors and illustrators to employ aesthetic distancing in children’s and YA literature. Aesthetic distancing includes the use of both visual and verbal techniques to provide a sense of what Jacqueline F. Eastman calls ‘controlled danger’ where the narrative becomes ‘comfortably exciting rather than overwhelming’ (75). By drawing on art and visual design theory and principles, this article will analyse where Speak: The Graphic Novel (2018) uses aesthetic distancing, but more importantly, where it is rejected as a mode of representation. I suggest that Andersen and the illustrator Emily Carroll take a middle ground approach, by which I refer to the fact that although aesthetic distancing is used, the narrative still presents threatening text and images to demonstrate that healing from sexual violence is an on-going process. Specifically, I analyse the jaggedness of the artistic style, vague pronoun usage and language, power relations, intertextuality, and the symbol of the mouth. I conclude by considering the ethical ramifications of employing aesthetic distancing in stories about sexual assault.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".