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Record W4366983554 · doi:10.3138/jh-2022-0041

A History of Military Engagements: The Contrasting Warfare Tactics of Frankish and Turkic-Syrian Field Armies

2023· article· en· W4366983554 on OpenAlexaffvenue
Peter del Rosso

Bibliographic record

VenueJournal of History · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBattleAncient historyLawHistoryBattlefieldInfantryPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to showcase the Battle of Dorylaeum, fought between the invading Frankish forces of the First Crusade and the defending Turkic-Syrian armies of the late eleventh-century in Anatolia, as a unique case study to explore the maladapted nature of the opposing factions’ modes of warfare. While the Frankish armies naturally favoured western tactics such as heavy cavalry charges and thus developed their entire marching and fighting system to protect the knightly core of their forces, their Turkic-Syrian opponents entered the battlefield in a rapid and erratic way which had led to their dominance in oriental warfare for decades prior. Upon a survey of the tactics used by both Frankish and Turkic-Syrian, based solely on the information evident through the relevant primary sources of the Battle of Dorylaeum, a twofold proof becomes apparent. Firstly, the maladapted nature of the two forces becomes incredibly apparent, stemming from the pitting of the heavily armed Frankish forces and the nomadic way of warfare utilized by their Turkic-Syrian opponents. Secondly, Dorylaeum comes to the forefront as the battle most representative of this maladapted nature; while the First Crusade is largely composed of sieges with remarkably few field battles, Dorylaeum is the first instance of both factions wielding their respective modes of warfare in a way which decided the fate of the following years of the First Crusade.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.279
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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