The Other Side of the Hill: Combat Intelligence in the Canadian Corps, 1914â1918
Bibliographic record
Abstract
For some, a discussion on military intelligence and the First World War is the ultimate oxymoron. They might ask: when and where did generals display any use of intelligence? That the Battle of the Somme continued beyond the first day, they might argue, demonstrates a complete lack of military intelligence, or any other type of intelligence for that matter. If there ever was a war, they might add, where donkeylike officers led lion-like soldiers to slaughter against barbed wire, machine guns, and trenches, then the Great War was it. The oft told story of how Sir Launcelot Kiggell, Sir Douglas Haig’s chief of staff, upon seeing the Passchendaele battlefield and its sea of mud and carnage, reportedly wept, “My God! Did we send men to fight in that?” only to be answered by an aid: “It’s worse further up,” has lent credence to the position that the British high command was, indeed, incompetent. The myth that British generals were donkeys is an old one, and not likely to disappear completely anytime soon—at least in popular imagination. However, a study of combat intelligence should help to dispel this myth, for when intelligence was used wisely—as it usually was in the Canadian Corps—it increased the likelihood of success in the field. It did this by dispersing some of the fog of war and the resulting battlefield confusion. Good intelligence gave planners the details necessary for preparing the incredibly complex set-piece battles that were the hallmark of First World War combat. Such meticulous care and precision preparation ensured that there were fewer surprises on Zero Day, the day of attack, then otherwise would have been the case. By cutting through the fog of war, intelligence reduced the assaulting troops’ dependence on circumstance and luck, while restoring to commanders some degree of control over events in what was an otherwise highly chaotic environment. This was no small matter, especially on battlefields where communications were painfully slow, erratic and unreliable. Officers and men of the Great War faced conditions and technological advances that had completely altered warfare from what they had expected and trained for. To compensate, the Canadians, and others, used combat intelligence to help overcome such obstacles as poor communications, heavy machine-gun and artillery fire, entrenchments and barbed wire, and came to see it as a crucial element in waging successful trench warfare.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 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".