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Record W2891788139 · doi:10.7202/1050721ar

Cast Down, But Not Forsaken

2018· article· en· W2891788139 on OpenAlexvenueaboutno aff
Elliot Worsfold

Bibliographic record

VenueOntario History · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsGermanAmbivalenceConversationIdentity (music)World War IIFace (sociological concept)HistoryResistance (ecology)SociologySpanish Civil WarPolitical scienceLawEthnologyGender studiesArtPsychologyPsychoanalysisAestheticsSocial scienceArchaeology

Abstract

fetched live from OpenAlex

This study seeks to reassess the notion that German-Canadians in Ontario were “silent victims” during the Second World War by exploring the wartime experience and memory of German-Canadian Lutheran congregations in Oxford and Waterloo Counties. Far from silent, Lutheran pastors initiated several strategies to ensure their congregants did not face discrimination and internment as they had during the First World War. These strategies encompassed several reforms, including eliminating German language church services and embracing English-Canadian symbols and forms of post-war commemoration. However, these reforms were often met with resistance and ambivalence by their congregations, thereby creating a conversation within the German-Canadian Lutheran community on how to reconcile its Germanic and Lutheran heritage with waging a patriotic war. While previous studies have primarily focused on identity loss, this study suggests that the debates that occurred within these Lutheran churches were representative of the community’s German-Canadian hyphenated identity.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.123
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0290.011
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0270.003

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 head0.025
GPT teacher head0.217
Teacher spread0.192 · 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
Published2018
Admission routes2
Has abstractyes

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