Haunted houses, haunted bodies, and vampires in Black women’s contemporary Gothic
Bibliographic record
Abstract
In this thesis, I examine how and why Black women writers, in England and in the US, use the Gothic landscape of haunted houses and haunted bodies, as well as the trope of the monstrous Other to engage with the trauma of slavery, racial discrimination, misogyny, sexuality, and identity.I examine the site of the haunted house, haunted bodies, and the figure of the vampire and vampiric consumption in Helen Oyeyemi's White is for Witching, Octavia E. Butler's Fledgling, and Tananarive Due's The Good House.In all three novels, hauntings are connected to trauma and loss.Additionally, in The Good House and White is for Witching, the site of the haunted house is connected to matrilineal curses.The figure of the vampire, and vampiric consumption, on the other hand, is representative of the fear of miscegenation, and the destabilisation of the binary between the self and the Other.In my introduction, I give an overview of the Gothic genre, tracing why the Gothic is the
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".