Bibliographic record
Abstract
The modern mind has demonized the bayonet as a weapon of war. Of all the popular images of the First World War, the most poignant is that of brave soldiers being sent “over the top” of their trenches with fixed bayonets for another futile charge on an enemy machine-gun emplacement. The bayonet has become, in some ways, a symbol for the frustration and futility of the war, as it seemed to be a weapon that should not have been there in the first place. Popular historians such as Pierre Berton have denounced it, claiming it to be, “as useful as a cutlass” on the modern industrialized battlefields of the Great War. What place could or should the direct descendant of the medieval pike have in the battle order of twentieth-century armies alongside weapons such as the machine gun? Why had this simple weapon not died out alongside the Napoleonic musket or the muzzle-loading cannon, yielding to the realities of technological progress? At first glance the survival of the bayonet as a weapon might seem like an anachronism to the historian, but here popular conceptions (or misconceptions) must be set aside. Whilst the bayonet charge may have become a symbol of the First World War, its use did not die in the trenches of northern France. Bayonet training was still a major part of infantry drill during the Second World War, and the United States armed forces continued to teach bayonet fighting up until at least the Vietnam War. Some
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".