Traumatic Geographies: Mapping the Violent Landscapes Driving YA Rape Survivors Indoors in Laurie Halse Anderson’s <i>Speak</i>, Elizabeth Scott’s <i>Living Dead Girl</i>, and E. K. Johnston’s <i>Exit, Pursued by a Bear</i>
Bibliographic record
Abstract
The rape of the land and of women has long been connected in literature and across cultures (Phillips). Young Adult (YA) sexual assault narratives are growing increasingly popular, and the location of attacks in such stories is significant, such as when rape is depicted in nature, because, in reality, much sexual violence occurs in the private sphere (Kerber)—the home (Altrows). Using an ecofeminist lens and conceptions regarding the treatment of sexual violence in children’s and YA literature (Marshall, “Stripping”; “Girlhood”), this paper examines Speak (Anderson), Living Dead Girl (Scott), and Exit, Pursued by a Bear (Johnston), which all set rape scenes outdoors. When individuals are violated in outdoor spaces, these sites can come to hold traumatic memories, prompting a shift in survivors’ relationships with the outdoors. For the protagonists in all three novels examined here, outdoor spaces come to represent pain, and so a critical consideration of the setting of sexual assault in these stories—particularly spaces with land, trees, and water—is warranted.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".