“Fitted to the Humour of the Age”: Alteration and Print in Swift’s <i>A Tale of a Tub</i>
Bibliographic record
Abstract
Alteration links seemingly disparate ideas and pieces of the text in Jonathan Swift’s A Tale of a Tub. In the Tale’s allegory, brothers alter their coats through over-embellishment. In the Tale’s digressions, the Grub Street narrator alters texts by overvaluing and reading only added commentary and prolegomena. The Tale’s material format also demonstrates surface alteration in its constant shifting between forms and in the changes Swift makes to the 1710 edition. Books and bodies alike are altered by layers of new surfaces in the Tale. Swift suggests that in both cases these exterior alterations possess the ability to disrupt and distort interiors, producing madness in bodies and misreading in books. Uneasy with the possibility of alterations unbalancing or destabilizing his meaning in an attempt to fit the text “to the humour of the Age,” Swift creates a work that possesses the potential to grow with material alteration. Any errors, additions, or changes to his text over time, even if Swift might despise them, validate his strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".