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Record W4310182076 · doi:10.33137/wij.v18i2.39680

Transformations and Emergent Themes in American Jewish Women’s History

2022· article· en· W4310182076 on OpenAlexvenueno aff
Hana G. Green

Bibliographic record

VenueWomen in Judaism A Multidisciplinary e-Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHistoriographyJudaismPoliticsReligiosityGender studiesJewish historySociologyFeminismField (mathematics)Jewish studiesHistoryPolitical scienceLaw

Abstract

fetched live from OpenAlex

This essay studies emergent themes in American Jewish women’s history and integrates them within the broader, often sustained, themes in the field of American Jewish women’s history. Beginning with a brief historiography of the field of American Jewish women’s history as it emerged in the late 1970s, this examination traces its transformation over the following decades and its evolution to today. In addition to presenting the field’s persistent themes, including work and domesticity (women’s roles in both the public and private spheres), politics and social activism, religiosity, and feminism, looking at key texts from both feminist scholars as well as recent works in the field of modern Jewish history, this essay highlights the emergence of new lines of scholarly inquiry spanning contemporary social, cultural, political, economic, and broad intersectional discourse. Moreover, this essay advances and emphasizes two clear patterns of emergent literature based on this assessment. First, that they reflect and have developed out of extant themes in Jewish American women’s history, and second, they are shaped by and entangled with nascent themes across diverse academic disciplines. Finally, this paper comments on the current positioning of the field and recommends future directions for research in the field of American Jewish women’s history.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.279
Teacher spread0.257 · 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.

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
Published2022
Admission routes1
Has abstractyes

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