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Record W4401451080 · doi:10.25071/1916-0925.40376

Joshua, King David, and the Flying Nun: Doodles and Reader Annotations in Post-Holocaust Yiddish Primers for Children

2024· article· en· W4401451080 on OpenAlexvenueno aff
Miriam Borden

Bibliographic record

VenueCanadian Jewish Studies / Études juives canadiennes · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsYiddishThe HolocaustArtArt historyHistoryJudaismPhilosophyTheologyArchaeology

Abstract

fetched live from OpenAlex

Students in Yiddish supplementary schools used texts produced by educators steeped in a diaspora nationalist pedagogy that reflected the ideological coupling of Yiddish and Yiddishkeit: the Yiddish language informed one’s sense of Jewishness. By the 1960s, doodles students left in their schoolbooks challenged this coupling of language and identity. Though it is generally supposed that Yiddish primers ultimately tell us more about the aspirations of adults than they do about the experiences of children, reading these texts together reveals that children evolved their own relationship between Yiddish and Jewishness that was far more subtle than what they encountered in their textbooks. Postwar primers emphasize maintaining the Yiddish language on American soil, to the exclusion of the external culture; children’s doodles argue that more important than preserving the language was locating Yiddishkeit in the culture around them.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.613
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.011
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.002
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 head0.031
GPT teacher head0.285
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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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