Bibliographic record
Abstract
This article takes a fresh look at Southey’s radical poetry of the 1790s in order to assess Southey’s mobilization of the tropes of political violence and atrocity. In the repressive antijacobin climate of the mid to late 1790s, radicalism was frequently associated with the sensational imagery of unbridled popular violence and regicide, but such propaganda misrepresented the ways in which radical authors like Southey used their texts precisely to explore and negotiate the problem of “justified” violence. The two texts I focus on areWat TylerandJoan of Arc, both of which imagine the bloody overthrow and destruction of a violent British state. But I show that beneath such a sensational vision (which may seem to explain whyWat Tylerwas not published) is a more complex and coded engagement with the contemporaneous debate about politics, violence and democracy, including issues such as plebeian chivalry, heroic martyrdom, divine punishment, and state terror. I also argue that the furore surrounding the radical pirating ofWat Tylerin the postwar period overlooked the fact that the text offers the reader various political fantasies and discourses of violence ranging from regicide to patriarchal self-defence, sacrificial defiance and statesmanlike moral reflection. I hope to show, therefore, that a more nuanced historicist approach to Southey’s early poetry in fact yields a more polysemic hermeneutics than has been appreciated by critics from the Romantic period to the present.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".