"Dark and dangerous designs": Tales of Oppression, Dispossession, and Repossession, 1770-1800
Bibliographic record
Abstract
Not unlike the inexplicable phantasm, the Gothic novel has appeared to materialize from nowhere. Few critics have been able to explain why Gothic novelists were fixated upon the tropes of persecution, oppression, and the reclaimed birthright or why indeed they sought to resurrect a seemingly regressive, escapist folk-tale-like form despite the success of the "realistic" novels of Fielding, Richardson, and Smollett. Even fewer have been able to explain why Gothic novelists displayed so much awareness of gender issues before the publication of Mary Wollstonecraft's Vindication of the Rights of Woman in 1792. This essay begins by taking a rare glimpse into British reformist discourses of the late eighteenth century, focusing on contemporary allegations of incipient despotism and the widened appeal for universal (male) enfranchisement while also examining the new populist discursive strategies deployed by reformist writers. It demonstrates how the central themes and discursive strategies of Gothic novels from 1770 through 1800 conform to those found in contemporary reformist writing despite their lack of overt references to politics. On a larger scale, this essay shows how political discourse affects the shaping of literary genre and, conversely, how genre affects the shaping of political discourse in the rise of the so-called public sphere.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.067 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".