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Record W2993314817

Sir Richard Turner and the Second Battle of Ypres, April and May 1915

2015· article· en· W2993314817 on OpenAlexaboutno aff
Mark Osborne Humphries, Lyndsay Rosenthal

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsBattleHistoryAncient history
DOInot available

Abstract

fetched live from OpenAlex

The Second Battle of Ypres remains one of the most contentious Canadian actions of the First World War, thanks in large measure to the uneven performance of Brigadier-Generals Arthur Currie and Richard Turner.Turner in particular has been singled out for criticism over three key decisions he made during the fighting in late April 1915.The George Metcalf Archival Collection in the Canadian War Museum's Military History Research Centre holds Turner's papers, including his letters and a diary written soon after the battle.Here those primary sources are re printed for the first time along with a brief analysis.They reveal a man struggling to come to terms with what happened at Second Ypres.T h e Se c o n d Ba t t l e of Ypres remains one of the most contentious Canadian actions of the First World War.On the one hand, there is the view that Canadian troops held firm when French soldiers ran from the gas attack on their left and persevered against difficult odds.1 On the other is the argument that while Canadian soldiers ultimately helped stem the tide of the German advance, Canadian commanders made poor decisions during the fighting which threatened the whole 1 Daniel Dancocks, Welcome to Flanders Fields: The First Canadian Battle of the Great War -Ypres 1915 (Toronto: McClelland and Stewart, 1988).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.010
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0100.018
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.018
GPT teacher head0.233
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2015
Admission routes1
Has abstractyes

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