Too Much With Us, Too Much Missing: Race, Coloniality, and Romantic Time
Bibliographic record
Abstract
Abstract For many decades, scholars of romantic-era literature have recognized their field’s complicity in the rise of racial capitalism and European imperialism. This essay wonders what romanticism has left to teach us about race, empire, and coloniality. Three recent books offer different approaches to this question. Tristram Wolff’s Against the Uprooted Word (2022) takes a measured approach: Wolff focuses on the naturalization of language in romantic philology and poetry, a turn that bolstered the civilizational aspirations of empire but which, he argues, offer a more varied set of temporal forms resistant to developmentalist thought. Deanna Koretsky’s Death Rights (2021) critiques the romantic myth of the suicidal genius as a touchstone of liberal antiblackness that persists today. In a more positive vein, Lenora Hanson’s The Romantic Rhetoric of Accumulation (2023) argues for romanticism’s critical vitality, excavating from romantic poetry and other social practices a rhetorical theory of subsistence that recognizes language as a means of survival in the face of dispossession. In spite of their disparate evaluations of romanticism as a field of study, Hanson, Koretsky, and Wolff share a predilection for historical juxtaposition, anachronism, and montage, techniques rooted in romantic-era experiences of the uneven temporalities of capital and empire.
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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 | 0.002 | 0.005 |
| 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 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".