Bibliographic record
Abstract
Abstract For over two hundred years of narrative culture, when female characters try to get together, crazy things happen. Indeed, the greater the means at women’s disposal, the more severe and twisted is the anxious reaction. But behind this broad anxiety lurks a powerful ideal of sympathetic and strategic female networks, an ideal that takes its intimate shape from the expectations of communications media, and that underwrites the very culture that would deny it. The book examines novelistic culture from the British novel to Hollywood film as a series of responses to the threat and promise of female networks. In texts from Clarissa, Emma, and The Portrait of a Lady to Sorry, Wrong Number, Vertigo, and You’ve Got Mail, it argues that a recurring gothic nightmare haunts plots of courtship and marriage, and that the concept of female networks illuminates the exits, for culture and criticism alike. And while this study must of necessity visit an uncanny realm of lost messages and false suitors, telepathy and artificial intelligence, locked rooms and time-traveling stalkers, these occult concerns only confirm the power at stake in the most basic modes of female communication, in gossip, letters, and phones.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".