Mingled Measures: Gothic Parody in Tales of Wonder and Tales of Terror
Bibliographic record
Abstract
Labeled by Louis Peck a “bibliographical hazard,” Tales ofTerror has long suffered from two misrepresentations: 1) it has been frequently attributed to M.G. Lewis, although no external evidence exists to support the claim; and, somewhat paradoxically, 2) it has been dismissed as a mere burlesque of Lewis’s Tales of Wonder, despite the fact that the majority of its poems treat Gothic themes in a serious manner. The parodic spirit pervading Tales of Wonder stems in part from Lewis’s attempt toanticipate and defuse critical alarm about his Gothic works. The writers of Tales of Terrorcarry on this double-edged treatment of the Gothic, especially in the volume’s“Introductory Dialogue” between a defender and opponent of Gothic poetry. Thedestabilizing presence of a satiric voice in ballads specifically selected for their recoveryof a more forceful, authentic, and native idiom of poetry also raises an interestingsecondary question: whether Gothic ballads can be free of the ironic consciousness theywere originally and ostensibly designed to exclude.
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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 | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".