On Geography and War: New Perspectives on the Ardennes Campaigns of 1940 and 1944
Bibliographic record
Abstract
We use examples from the European theater in World War II to argue that the assumption that combat is typically chaotic yields only limited insight into the large-scale evolution of military operations. To do this we examine the Ardennes campaigns of 1940 and 1944 in the context of explanatory devices used in physical geography such as complexity, nonlinearity, and emergence. We show that during the successful 1940 offensive that eventually led to the fall of France, the Germans were operating close to a set of thresholds in what we call the strategic space; the success of the offensive was contingent on a rapid advance and outmaneuvering of the Allied forces. In the readily defensible tactical space of the narrow Ardennes valleys, small changes in the conduct of or response to the German advance could have forced delays with profound consequences for the campaign. In 1944, by contrast, the Germans were not operating close to a system threshold and the attacking columns were frequently delayed or halted by determined resistance. Even if resistance had been weak, however, a breakout to Antwerp is unlikely to have been sustainable given the superiority in Allied power and the crippling supply problems facing the Germans.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".