MétaCan
Menu
Back to cohort
Record W3158618731 · doi:10.24908/iqurcp.7725

Identities, Communities and Belonging: The Effects of Violence and Trauma on Jewish Women

2017· article· en· W3158618731 on OpenAlexvenueaboutno aff
Elana Finestone

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsJudaismAgency (philosophy)LesbianContext (archaeology)ShalomGender studiesSociologyThe HolocaustDomestic violenceCriminologySexual assaultSocial psychologyPsychologyPoison controlSuicide preventionLawPolitical scienceMedicineHistorySocial scienceTheology

Abstract

fetched live from OpenAlex

This paper helps to explain why sensitivity to cultural context matters in terms of violence and trauma such as sexual assault, sexual abuse, rape, spousal abuse, assault, and / or surviving the Holocaust. My interest in this paper was stimulated through my volunteer work at a Family Service Centre for Jewish women in Ottawa called Shalom Bayit. This particular volunteer experience helped me to understand that cultural location determines how people express a violating and harmful experience and the type of support they receive. This paper will explore different cultural locations as well as agency to civil, criminal and religious law. Women’s agency plays a role in how Jewish women’s experiences are voiced and validated by their surrounding community. The following research has implications for professionals working with violated and traumatized women. This paper will be using a comparative method researching Jewish women with different identities. Examples of identities to be examined are Secular, Orthodox, Israeli, Lesbian, and Sephardi Jewish women.

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)0.004
metaresearch head score (Gemma)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.012
Scholarly communication0.0050.002
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.091
GPT teacher head0.375
Teacher spread0.284 · 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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInquiry Queen s Undergraduate Research Conference ProceedingsSame topicJewish and Middle Eastern StudiesFrench-language works237,207