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Record W7154507678 · doi:10.23777/sn0119/art_jsza01

Between Self-Defense and Loyalty

2019· article· en· W7154507678 on OpenAlexaff
Judith Szapor

Bibliographic record

VenueS I M O N Shoah Intervention Methods Documentation · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsMcGill University
Fundersnot available
KeywordsAntisemitismNumerus claususJudaismLoyaltyRacismRacial integrationAssimilation (phonology)

Abstract

fetched live from OpenAlex

Enacted in September 1920 in Hungary, the numerus clausus law, the first antisemitic law in postwar Europe introduced discrimination against Jews in higher education. Ostensibly a remedy for the “overcrowding” of universities, the law breached the previous, liberal era’s concept of equal citizenship. This survey of Jewish responses to the law between 1920-1928 is based on the coverage of Egyenlőség, the representative weekly of assimilated, Neolog Jews. The arguments voiced by contemporary commentators against the numerus clausus law highlight their precarious position, between fighting to maintain full membership in the Hungarian nation while also nurturing a sense of Jewish identity; ultimately, they reflect their views on the prospect of assimilation itself.

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)0.005
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.039
Scholarly communication0.0080.006
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.064
GPT teacher head0.396
Teacher spread0.331 · 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
Published2019
Admission routes1
Has abstractyes

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