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Record W2897002184

Shifting Attitudes: Torontonians and Their Response to the Great War

2017· article· en· W2897002184 on OpenAlexaffvenueabout
Chelsea V Barranger

Bibliographic record

VenueThe Graduate History Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsMcMaster University
Fundersnot available
KeywordsXenophobiaScholarshipHome frontSpanish Civil WarWorld War IISociologyEthnic groupJust war theoryHistoryPolitical scienceGender studiesRacismLaw
DOInot available

Abstract

fetched live from OpenAlex

There exists little historical scholarship on Toronto during the First World War, or the impact of the war on its citizens. An examination of various tensions and oppositional activities in Toronto during the war complicates current interpretations of a 'united front' in the city. While the City of Toronto was 'united' in the sense that the majority of Torontonians supported the war effort in theory, between 1914 and 1918 there were serious debates and disagreements along various dividing lines regarding what support for the war constituted and required. The focus on homogeneity within the literature has resulted in a lack of analysis of the marginalized groups within the city, as well as the divides that existed within the British-Protestant community itself. The story of Toronto during the war is one of perceived unity, but in reality the city was rife with extensive divisions along national, ethnic, gendered, and religious lines. Far from uniting the city, the war brought forth long held tensions and xenophobia to the surface, resulting in violence in the streets of Toronto.

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)0.002
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: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.502

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0130.020
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.114
GPT teacher head0.312
Teacher spread0.198 · 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
Published2017
Admission routes3
Has abstractyes

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