Old & New <i>Foundling Hospitals for Wit</i> in the Age of the Digital Miscellanies Index
Bibliographic record
Abstract
Literary miscellanies have long been popular. Richard Tottel's 1557 collection, Songes and Sonettes, gathered the works of various Tudor poets. In 1684, Jacob Tonson and John Dryden launched a miscellany that reached six volumes by 1709. By 1743, the time was ripe for a satirical miscellany; Sir Robert Walpole's reign as Britain's prime minister had just come to an end, and Pope revised his Dunciad to devastating effect. Out of the mix of political uncertainty and satiric excess emerged The Foundling Hospital for Wit, which ran to six volumes by 1749. It offered a potpourri of parodies set to old tunes and prose extracts haranguing the new administration and promoting amputation with Swiftian gusto. One of the problems facing twenty-first-century readers is how to get a handle on what exactly is being satirized. In order to gain a fuller understanding of how this collection and its sequel, The New Foundling Hospital for Wit (1768-73), came about, we need to probe its printers, booksellers, and editors, whose names, more often than not, are omitted or disguised. Ornaments provide one key to determining who printed what. The Foundling Hospital for Wit appears to have been the brainchild of Sir Charles Hanbury Williams. The New Foundling Hospital for Wit starts off with his poem, “Isabella,” which stands up well beside Rape of the Lock. A vehicle for John Wilkes and his radical bookseller John Almon, this later miscellany offered up the most audacious satires and politicized engravings of its time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.451 | 0.221 |
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".