Describing the Enemy in the First Crusade: The Rhetoric of Innumerable Hosts
Bibliographic record
Abstract
This article explores the rhetorical and ideological function of the motif of innumerable enemy hosts in Latin accounts of the First Crusade. Drawing on eyewitness narratives such as the Gesta Francorum, Peter Tudebode’s Historia de Hierosolymitano itinere, Raymond of Aguilers’s Historia Francorum qui ceperunt Iherusalem, and Fulcher of Chartres’s Historia Hierosolymitana, the study demonstrates that descriptions of the enemy as overwhelmingly numerous were not factual reports but deliberate literary strategies. These hyperbolic portrayals served to frame the Crusaders’ military efforts as miraculous, divinely sanctioned triumphs of the few against the many. The article traces the biblical roots of this motif, focusing particularly on narratives such as the defeat of the Midianites by Gideon (Judges 7–8) and King Asa’s battle against Zerah the Ethiopian (2 Chr 14), and explores its development in medieval Christian exegesis, notably in the works of Gregory the Great and Hrabanus Maurus. The enemy’s multitude is further emphasized through ethnic catalogues, which function to reinforce perceptions of otherness and chaos in contrast to Christian unity and divine favour. The study argues that these narrative patterns reflect a shared topos that shaped medieval perceptions of the Crusades, while also contributing to the formation of a mythologized collective memory in Latin Christendom.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".