TO WHOM GO THE SPOILS?: EXPLAINING 4,000 YEARS OF BATTLEFIELD VICTORY & DEFEAT
Notice bibliographique
Résumé
The cruel nature of war gives reason for its study. A crucial component of this research aims to uncover the reasons behind victory and defeat. Winning, after all, is the central attraction of organized violence. Unfortunately, political science efforts in this direction have been rare, and the few theories on offer (numerical preponderance, technology theory, and proficiency) are infrequently tested against the empirical record. This dissertation therefore not only subjected the main theories of battlefield victory to a systematic test against the historical record, but also did so with a dataset more comprehensive and with greater chronological breadth than any other in the political science literature. The range of battles included runs from Megiddo (1469 BC) to Wanat (2008). \n\nSuch a historically ambitious undertaking is unfortunately fraught with a series of methodological concerns. However, fears regarding the reliability of these historical statistics are best allayed by the assortment of historiographical techniques that have been used to eliminate the more dubious estimations. Concerns regarding data validity are similarly met with a clear delineation of methodological scope: current data is both western-centric and fails to speak to combat in pre-agrarian settings; the conclusions drawn below therefore keep a recognition of these limitations in mind. \n\nUltimately, the chief findings of this study are that neither Napoleon’s ‘big battalions’ nor armies boasting technological supremacy over their rivals are assured any guarantee of battlefield success. This result is a powerful blow to both mainstream realist theory (whose power calculations rely on raw aggregations like army size) and Western defence planners (who have predicated their strategies on the belief that technology is the chief underpinning of victory). That being said, the most compelling causal explanation for battlefield victory, combat proficiency, appears subject to a crucial caveat: even the most talented armies can be ground into dust. This finding will provide little comfort to gifted armies that find themselves involved in a costly and prolonged campaign, such as Canada and America in Afghanistan. Lastly, this project’s contribution should be seen as not only theoretical and practical in nature, but also as providing a methodological toolkit and empirical resource of use to anyone subsequently interested in tracing the evolution of organized violence over time.\n\nIn short, this project is summation of how political science thinks about the most basic aspect of war: battle. As the findings of this dissertation suggest, what is distinctly troublesome is that our existing theories and assumptions about who wins and why appear to bear little resemblance to reality. If anything, this dissertation calls attention to the urgent need for further research into the matter of battle victory.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».