John Weever, <i>Ancient Funeral Monuments</i> (1631)
Bibliographic record
Abstract
About the author John Weever (1575/6–1632) endeavoured to launch a literary career in London, where he became embroiled in the Poetomachia, the notorious quarrel of the Elizabethan playhouses. He wrote three books of a satiric bent and two of a religious nature, before publishing his work on epitaphs three decades later. About the text If the seventeenth century saw the ascendancy of the epitaph as a genre, Weever's 900-page folio constitutes the genre's greatest treasure trove and shrine. This massive contribution to early modern antiquarianism catalogues funeral monuments from the dioceses of Canterbury, Rochester, London and Norwich by compiling transcriptions and observations gleaned from Weever's studies and travels. In his epistle to the reader, Weever acknowledges the network of scholars and libraries that made possible his work and even goes so far as to invite his readers to collect, on his behalf, notes of local inscriptions; he not only travelled extensively throughout England and Scotland but also visited continental sites in order to gather material for his research. His book, however, should not be anachronistically read through the prism of any modern presupposition of historical objectivity. It disposes textual artefacts into a compendium stamped with his own style and personality – somewhat in the vein of those polymathic digressers Burton and Browne. Weever enunciates an early imperative for the preservation and conservation of stone records out of a desire to protect the nation state through memory. The arts of memory The two excerpts are taken from Weever's prefatory discourse, where he expounds upon the history of ancient and medieval burial rites, funeral monuments and sepulchres, besides the vandalism and violation done to these structures. The first excerpt – the beginning of the opening chapter ‘Of monuments in general’ – spans the broad terrain that the word ‘monument’ covers in the period. All cultural work seems to have a monumentalising capacity. Ultimately, Weever privileges inscription over visual image not just because he had a poet's ear for verse but because he was following the lead of his fellow antiquarians, who conceived of the monument in terms of epigrammatic wit. Weever thus offers little description of the architecture of the tombs he visited, concentrating instead on the epitaphs that they bore. In this regard, his project walks a fine line.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".