Into the Wilderness: The Fleeing Figure in Early Modern Heroic Poetry
Bibliographic record
Abstract
All the major epic poems from the Renaissance include a marginalized character who tries to escape from the action. The fleeing figure appears in Ludovico Ariosto’s Orlando furioso, Torquato Tasso’s Gerusalemme liberata, Edmund Spenser’s The Faerie Queene, John Milton’s Paradise Lost, and his Paradise Regained. Each iteration of the fleeing figure trope gets a little closer to attaining freedom from their oppressive social contexts until the limitations of the epic/romance polarity are finally dismissed in Milton’s brief epic. This study identifies the fleeing figure trope in early modern epic romance poetry as a site of resistance against imperial discourse and explores how its development eventually undermines the epic genre. The retreat of marginalized, chivalric romance characters from their epic narrative contexts can be understood as a rejection of consumptive narratives that are hostile to the individual subject. This research draws upon Lacanian psychoanalytic theory and David Quint’s theories of epic continuity and the tension between epic and romance (Epic and Empire, 1983) to identify and trace the generic mediation of imperial epic and chivalric romance through early modern heroic poetry. As each successive poem moves further away from polarizing gender constructs, the evolving feminist critique of heroic narratives that movement represents leads to the absolute rejection of imperial discourse. This research traces the mediation between imperial narratives and individual autonomy through the maturation of heroic poetry as it outgrows the limitations of simplified gender assumptions.
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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 | 0.003 |
| 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 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".