Bibliographic record
Abstract
In sixteenth-century Hungary, Countess Elizabeth Báthory tortured and killed over six hundred servant girls in order to bathe in their blood; she believed this brutal ritual would preserve her youth and beauty. Danica, a young forensic psychologist, is drawn to Báthory’s legend. She has moved from Canada to England to work at Stowmoor, a Victorian insane asylum turned modern-day forensic hospital. One of her patients, the notorious Martin Foster, murdered a fourteen-year-old girl in homage to Báthory. He cultivates his criminal celebrity, and Danica struggles to maintain a professional demeanor with the charismatic Foster as she begins to suspect that his activities may be linked to a cabal that idolizes the countess. Danica’s life in London becomes increasingly complicated when Maria, a glamorous friend from Danica’s past, arrives to do archival work in the city. She claims to have discovered Báthory’s long-lost diaries and she slowly reveals to Danica the horrific, yet fascinating passages. As Danica’s career and her relationship with her artist-boyfriend, Henry, falters, Maria lures her into a complex social sphere. Unsure of whom to trust as her professional and personal lives become dangerously entwined, Danica must decide what she is willing to risk to satisfy her attraction to Báthory’s ominous legend.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.457 | 0.233 |
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".