El lopismo inglés del siglo XVIII: Sir John Talbot Dillon (1739-1805) y William Hayley (1745-1820)
Bibliographic record
Abstract
English opinions about Lope de Vega during the last quarter of the eighteenth century are extremely important to understand not only how Lope’s image in this country evolved, but also to appreciate to what extent Lope became a battlefield in the European-wide controversy about rules and good taste. To illustrate this conflict, the present article examines the opinions about Lope that we can find in the work of two eighteenth-century Hispanists, John Talbot Dillon (1739-1805) and William Hayley (1745-1820). Studying them in the context in which they were written, and contrasting their sources and criteria, we explain the meaning and importance of these texts in their European context, and we offer in an appendix a translation so that specialists can compare them with the work of the well-known English lopistas of the nineteenth century (Lord Holland, Shelley, etc.).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".