Little Puggies: Consuming Cuteness and Deforming Motherhood in Susan Ferrier’s <i>Marriage</i>
Bibliographic record
Abstract
Frequently represented as substitutes for children by eighteenth-century satirists and moralists, lapdogs stood accused of distracting their mistresses from maternal obligations. These women supposedly projected the feelings and desires of children onto their canine companions. In Susan Ferrier’s Marriage (1818), the target of this animal-commodity fetishism is the pug dog. Why was this particular lapdog so well-suited to the attentions of consumers and critics, and what might “ugly” animals beloved by people tell us about human tastes? Reading contemporary aesthetic theory alongside eighteenth-century literary and material culture reveals that the quality identified today as “cuteness” was considered a factor in women’s affection for certain pets. Just as aesthetic theorists find freakishness to be concomitant to cuteness, so too did critics of these dogs discuss the pug’s “deformity.” Current debates about the moral worth of cuteness likewise have eighteenth-century analogues. In Marriage, Juliana Douglas’s interactions with her companion animals and their ceramic simulacra reveal the threat posed by the cute and its ability to collapse distinctions between objects, animals, and people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".