TO WHOM GO THE SPOILS?: EXPLAINING 4,000 YEARS OF BATTLEFIELD VICTORY & DEFEAT
Bibliographic record
Abstract
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.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".