Bibliographic record
Abstract
This historiographical essay explores how scholars have analyzed depictions of the Crusades in literature. Specifically, it compares how three books, Geraldine Heng’s Empire of Magic: Medieval Romance and the Politics of Cultural Fantasy, Lee Manion’s Narrating the Crusades: Loss and Recovery in Medieval and Early Modern English Literature, and Marisa Galvez’s The Subject of Crusade: Lyric, Romance, and Materials, 1150 to 1500, examine the significance of Crusade discourse’s prevalence in literature, including in a medieval Arthurian romance, a fictional account of Richard the Lionheart’s crusading experiences, and even a Shakespearean play. Examining the Crusades through literature alone is an insufficient way to reach historical conclusions. However, by analyzing fictional Crusade narratives within their historical contexts, each book, this paper argues, unveils invaluable insights into medieval society’s perception of the disturbing ethical and cultural issues that surrounded the Crusades, such as crusader cannibalism, and how contemporaries tried to mitigate these concerns by writing reimagined histories.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.156 | 0.056 |
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".