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Sam Hughes and Overcoming Recruiting Crisis of Canadian Army at Height of First World War (1915—1916)

2022· article· en· W4283167298 on OpenAlexaboutno aff
Е.С. Симоненко

Bibliographic record

VenueNauchnyi Dialog · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsMobilizationContext (archaeology)HistoriographyPolitical scienceLawSpanish Civil WarWorld War IIManagementHistory

Abstract

fetched live from OpenAlex

The process of overcoming crisis phenomena during the recruitment of the Canadian volunteer army at the height of the First World War is analyzed in the context of the activities of the Minister of Militia and Defense Sam Hughes. The chronological framework is due to the beginning of the crisis in the recruitment of the Canadian volunteer army (October 1915) and the completion of S. Hughes’s activities as Minister of Militia and Defense (November 1916) in connection with the forced resignation. The relevance of the study is due to the fact that for the first time in Russian historiography, views are formulated and the mobilization activity of Minister S. Hughes is analyzed during the crisis in the recruitment of the Canadian volunteer army at the height of the First World War. The efforts of S. Hughes to overcome the recruitment crisis and stimulate recruitment into the ranks of the Canadian Volunteer Army are traced. The reaction of the press and the public to the mobilization activity of S. Hughes at the height of the war is studied. The reasons, circumstances and consequences of the resignation of S. Hughes from the post of minister are clarified. It is proved that during the leadership of the War Department, S. Hughes managed to achieve impressive results, however, due to his stormy temperament and personal ambitions, he often went beyond his powers, which ultimately predetermined his resignation.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.155
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0270.014
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.211
Teacher spread0.180 · 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 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
Published2022
Admission routes1
Has abstractyes

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