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Record W2605064412 · doi:10.18432/r2005v

The "Draw-A-Religious Jew" Test and Students’ Religious Identities

2017· article· en· W2605064412 on OpenAlexvenueaboutno aff
Matt Reingold

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingJudaismReligious identityReligious educationDiversity (politics)Identity (music)PsychologyCultural pluralismThe artsPluralism (philosophy)Test (biology)SociologySocial psychologyRelation (database)Gender studiesPedagogyAestheticsEpistemologyVisual artsTheologyAnthropologyReligiosityArtPhilosophy

Abstract

fetched live from OpenAlex

A quantitative arts-based study was conducted with high school juniors and seniors at a community Jewish school in Toronto. This group represented a diverse mixture of students who populate the school in relation to gender, involvement in school life and religious denominations. Students were prompted to draw a religious Jew and the images were scored based on five different markers. Of the 35 drawings, only one female was drawn. Additionally, the majority of students drew charedi Orthodox Jews, despite none being present in the study group. The article concludes by addressing the problem with how students understand the word religious and offers suggestions for how to reframe religious identity in a way that reflects pluralism and denominational diversity.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.072
GPT teacher head0.400
Teacher spread0.328 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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