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Record W6995527820

The Other Side of the Hill: Combat Intelligence in the Canadian Corps, 1914â1918

2012· article· en· W6995527820 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIntelligence, Security, War Strategy
Canadian institutionsnot available
Fundersnot available
KeywordsBattleBattlefieldCredenceMilitary intelligenceMilitary tacticsFirst world warWorld War II
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.264
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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