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Record W2328062071 · doi:10.1515/zaa.2011.59.2.109

‘And I Did Want to Pass’: Reading Canadian Second Generation Holocaust Memoirs as Migration Texts

2011· article· de· W2328062071 on OpenAlexaboutno aff
Nina Fischer

Bibliographic record

VenueZeitschrift für Anglistik und Amerikanistik · 2011
Typearticle
Languagede
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMemoirThe HolocaustImmigrationHolocaust survivorsJudaismReading (process)SociologyTransgenerational epigeneticsAlienationHistoryIdentity (music)Gender studiesLiteratureAestheticsArtPolitical scienceArt historyLaw

Abstract

fetched live from OpenAlex

One aspect of post-Holocaust Jewish life to which little attention has been paid in the study of Holocaust literature is the experience of migration. This article examines three Canadian Second Generation Holocaust memoirs and their portrayal of migration. Memoirs by Jewish-Canadian authors prove to be particularly beneficial for analyzing aspects of migration because the immigration of Canada’s survivors often took place when their children were old enough to consciously experience it. Lisa Appignanesi’s Losing the Dead (1999), Eva Hoffman’s Lost in Translation (1989), and Elaine Kalman Naves’s Shoshanna’s Story (2003) all depict the challenges of the arrival to 1950s Canada. The memoirs explore the ways in which the young immigrants cope with dislocation, alienation, and belonging. Against the backdrop of a traumatic family history, they experience different forms of ‘cultural crossings’ - for instance, with regard to language, the immigrant’s body, or religious identity. The focus on migration in Second Generation memoirs highlights the transnational and transcultural rather than merely the transgenerational features of Holocaust memory.

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)0.008
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: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.518

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0300.019
Scholarly communication0.0110.004
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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
Published2011
Admission routes1
Has abstractyes

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Same venueZeitschrift für Anglistik und AmerikanistikSame topicCanadian Identity and HistoryFrench-language works237,207