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Record W2769863008 · doi:10.15290/cr.2016.14.3.06

The witness of the unspoken experience: Postmemory in Bernice Eisenstein’s I Was a Child of Holocaust Survivors

2016· article· en· W2769863008 on OpenAlexaboutno aff
Aleksandra Kamińska

Bibliographic record

VenueCrossroads A Journal of English Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsWitnessThe HolocaustHolocaust survivorsArtPsychologyGender studiesSociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

In the graphic memoir I Was a Child of Holocaust Survivors (2006) Bernice Eisenstein examines her identity as a second generation survivor, tells stories about her parents, and depicts the community of survivors in Toronto. Eisenstein's memoir is most often described as a graphic novel. However, the book is a specific combination of words and drawings, and can be hard to categorize. In my paper I focus on Eisenstein's complex relationship with her father presented in the novel, and argue that the way she writes about him and draws him is anchored in his unsaid Holocaust experience. I read Eisenstein's portrayal of her father in reference to the concept of postmemory, and suggest that Eisenstein was heavily affected by her father's experience of being a Holocaust survivor. Her deep connection to the past is demonstrated in I Was a Child of Holocaust Survivors through drawings, selected memories, and references to numerous works of culture. I discuss how Eisenstein draws her father and how she commemorates him in images -not as a victim, but as extremely strong personas: movie star, gangster or sheriff. I analyze the role of shtetl culture in the memoir as another way of linking present with past. I suggest that the books and movies about the Holocaust which Eisenstein references in the memoir create a basis for changing the confusing, or even unexpressed traumas, into an understandable story.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.327
Teacher spread0.296 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCrossroads A Journal of English StudiesSame topicMemory, Trauma, and CommemorationFrench-language works237,207