Weissbourd, Emily. Bad Blood: Staging Race Between Early Modern England and Spain
Bibliographic record
Abstract
Emily Weissbourd's Bad Blood: Staging Race Between Early Modern England and Spain interrogates Spain's infamous limpieza de sangre and how it was imagined by early modern England.Weissbourd highlights Spain's investment in white supremacy while drawing attention to the nuances of how blood purity was understood, imagined, and represented in relation to England's attempts to position themselves as innocent of racism.Weissbourd starts with the provocation that "Spain [is framed] as a site of alterity to be investigated within the Anglosphere" (2).Specialists in early modern England will be aware of how Spain is often negatively positioned in the early modern English imaginary, though Weissbourd's book complicates England's narrative by attention to Spanish archives.What distinguishes Weissbourd's analysis is her attention to racialized slavery to consider representations of Blackness and religious difference across the English and Spanish early modern canon.Such attention to Blackness upsets more binaristic understandings of "pure blood" where in certain literary examples Blackness actually implies a lack of impure descent due to the fact that "Blackness […] cannot be concealed" (11).As evidenced by recent monographs such as this recent work by Weissbourd and by Noémie Ndiaye's Scripts of Blackness: Early Modern Performance Culture and the Making of Race (Philadelphia: University of Pennsylvania Press, 2022), work beyond the Anglosphere deepens the study of race and empire in the premodern.Chapter 1 begins with Lope de Vega's La vilana de Getafe that highlights the fragility of pure descent as Inés is able to trick her beloved Félix into returning to her by spreading a false rumour that he is morisco.A fear infecting the possibility of "impurity" is whether it can be visibly recognizable, which returns to how Félix, who is not morisco, can be perceived as so if enough people suggest it.Purity of blood also interlocked with class, where an overfocus on purity could give "excessive" social mobility for those claiming pure blood.The play presents blood purity as an "invisible essence" comically positioned simultaneously with the apparent visible differences of moriscos' bodies and composure (31).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".