Race-Making and Romanticism: Notes on Pedagogy and the Position of Whiteness
Bibliographic record
Abstract
Recent discussions often conflate the project of developing an antiracist Romantic studies with the project of discovering an antiracist Romanticism, the elements of which could be culled from its revolutionary texts and radical figurations. This essay considers the risks of this conflation and the critical investments it protects. First, I discuss how and why some scholars are liable to skip over the task of considering the extent of Romanticism’s racism. Next, I relate my experience teaching an upper-level undergraduate course titled “Race-Making and Romanticism,” in which we explored how Romantic literature registered and participated in the early nineteenth-century project of forging whiteness and consolidating logics of anti-Blackness. Finally, I reflect on how my approach to antiracist work is a function of my position as a white cis male, suggesting that those of us in the position of whiteness would do well to consider our attachment to Romantic authors and texts as a structural relation that exceeds the dynamics of identification and disavowal.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.060 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".