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Record W4210704817 · doi:10.5771/9783956507229-333

Interview mit George Ellenbogen

2020· book-chapter· de· W4210704817 on OpenAlexaboutno aff
Brigitte Wallinger-Schorn

Bibliographic record

Venuenot available
Typebook-chapter
Languagede
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)MemoirArt historyImmigrationJudaismArtHistoryMedia studiesSociologyArchaeology

Abstract

fetched live from OpenAlex

The Jewish-Canadian and Arab-American writers and professors of literature George Ellenbogen (*1934) and Evelyn Shakir (1938–2010) were life companions. In both their memoirs, the authors tell stories of neighborhood, enriching encounters and their search for roots. George grows up in the Jewish immigrant quarter of Montreal, goes to McGill University, and later travels to the places of his ancestors, the destroyed world of the shtetl. In her Boston childhood, Evelyn is perceived as an Arab who does not entirely belong. As visiting professor in Arab countries, however, her students see her as an American. The memoirs, three related articles, and an interview with George Ellenbogen raise basic questions of belonging and otherness, cultural location and the pursuit of mutual understanding and respect. The volume also appeals to teachers who want to turn their lessons into contact zones in which different cultures and perspectives collide and enter into mutual dialogue. With contributions by George Ellenbogen; Pascal Fischer, Christoph Houswitschka; Sally Michael Hanna; John Kinsella; Margueritte Murphy; Evelyn Shakir (†); Brigitte Wallinger-Schorn

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0560.016

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 head0.039
GPT teacher head0.227
Teacher spread0.188 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same topicThemes in Literature AnalysisFrench-language works237,207