Fashionable Fictions and the Currency of the Nineteenth-Century British Novel
Bibliographic record
Abstract
Revealing how a modern notion of fashion helped to transform the novel and its representation of social change and individual and collective life in nineteenth-century Britain, Lauren Gillingham offers a revisionist history of the novel. With particular attention to the fiction of the 1820s through 1840s, this study focuses on novels that use fashion's idiom of currency and obsolescence to link narrative form to a heightened sense of the present and the visibility of public life. It contends that novelists steeped their fiction in date-stamped matters of dress, manners, and media sensations to articulate a sense of history as unfolding not in epochal change, but in transient issues and interests capturing the public's imagination. Reading fiction by Mary Shelley, Letitia Landon, Edward Bulwer-Lytton, W. H. Ainsworth, Charles Dickens, Mary Elizabeth Braddon, and others, Fashionable Fictions tells the story of a nineteenth-century genre commitment to contemporaneity that restyles the novel itself.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".