<scp>Olivia Ferguson</scp>. <i>Caricature and Realism in the Romantic Novel</i>
Bibliographic record
Abstract
The best known caricatures of this period came from the pens, brushes, and engraving tools of people like Gillray, Rowlandson, and Cruikshank (among others), who often incorporated their caricatures into larger set pieces of political satire. Those larger political satires unsettled and expanded the imaginary of the British imperial war state during the revolutionary and Napoleonic wars. To move from that famous graphic material to the period’s literary texts requires intellectual and imaginative deliberation, as well as an extended or modified metric of valuation. Both are in evidence throughout this book. Ferguson herself states that what she has provided is a history of ‘literature’s caricature in the Romantic period’ and of how caricature plays out in the period’s ‘comic, historical and horror fiction’. The idea is that this will grant us access to ‘literature’s own imaginative strength and substance’ when set against the (appropriately) ‘gigantic character of the age’. Making sense of literary caricature in this period proves to be an elusive and tricky task, largely owing to the sheer difficulty of defining quite what a literary ‘caricature’ is in the first place—as distinct from what we might find in the case of the artists wielding their sharp and threatening engraving tools. Chapter 1 is preoccupied with precisely that difficulty: what is a literary caricature, does it have a history, and in what senses can we legitimately refer to a literary approximation of something which is more obviously defined as an ancillary of graphic satire? We are told that Johnson never defined caricature in his dictionary, but Ferguson also tells us that a definition was added to that dictionary in an expansion published in 1818. This is the sort of detail that animates Ferguson’s larger argument about the development of a new consciousness for literary caricature in the target period. Ferguson insists that ‘Georgian satirical prints’ should ‘not be allowed to dominate any discussion’ of caricature because they were always part of a ‘cultural phenomenon with close connections to literature’. Ferguson looks at a whole range of dictionaries in search of ‘caricature’ as a stabilized category of art or literature (or both). Instead, she finds a whole range of definitional ambiguities and contradictions which were transmitted into the period’s written literatures.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.013 |
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".