MétaCan
Menu
Back to cohort
Record W1517666279 · doi:10.25071/1916-0925.19981

Postmemorial Positions: Reading and Writing After theHolocaust in Anne Michaels’s Fugitive Pieces

2003· article· en· W1517666279 on OpenAlexvenueno aff
Marita Grimwood

Bibliographic record

VenueCanadian Jewish Studies / Études juives canadiennes · 2003
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeThe HolocaustRepresentation (politics)Interpretation (philosophy)Reading (process)LiteraturePoetryHistoryWifeArtAestheticsLinguisticsPhilosophyLawPolitics

Abstract

fetched live from OpenAlex

Anne Michaels’s novel, Fugitive Pieces, has been criticized for its highly poeticized representation of the Holocaust. In this essay, however, Marita Grimwood argues that the novel uses structures of narrative transmission to explore precisely the difficulties of representing history and trauma in language. Grimwood proposes that the representation of three key characters is central to this undertaking. First, Jakob Beer, the child survivor and poet who narrates two thirds of the novel, is positioned as an intergenerational mediator, belonging fully neither to a pre-war nor a postwar generation. Two further characters (Ben, the child of survivors who narrates the end of the novel, and Michaela, Jakob’s second wife) symbolize the figure of the reader after the Holocaust, negotiating a link to the past through their interpretation and witnessing of Jakob’s life. The novel recognizes the problems inherent in communicating meaningful knowledge of past events to those living in the present. Yet, partly through Jakob’s vocation as a poet, it proposes also that poetry is a tool, however imperfect, for the communication of such knowledge.

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)0.005
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: Other · Consensus signal: none
Teacher disagreement score0.113
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.019
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.284
Teacher spread0.254 · 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
GenreOther

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

Citations3
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Jewish Studies / Études juives canadiennesSame topicMemory, Trauma, and CommemorationFrench-language works237,207