Understanding Jewish Art Jewishly: A Rationale and a Model for Including Jewish Art in Canadian Post-Secondary Coursework
Bibliographic record
Abstract
Abstract: This paper surveys literature in art education that explores cultural inclusivity. It then surveys Jewish Canadian history in order to provide a sketch of the cultural context, providing a rationale for teaching Jewish art at Canadian universities. A brief history of the nature of Jewish art and its relationship to that of the dominant cultures in which Jews have lived will be described. It proposes a model for teaching Jewish art and art by Jewish artists in Canadian universities that can provide students with opportunities to truly understand the cultural context in which this work is created, using Israeli-Canadian Sylvia Safdie’s Dust as an example. Keywords: Diversity; Inclusivity; Jewish Art; Sylvia Safdie. Résumé : Cet article se penche sur la littérature du domaine de l’enseignement des arts qui traite d’inclusivité culturelle. On y explore ensuite l’histoire juive canadienne pour dresser un tableau du contexte culturel et fournir un motif d’enseigner l’art juif dans les universités canadiennes. Y sont décrits un bref historique de la nature de l’art juif ainsi que sa relation avec les cultures dominantes au sein desquelles évoluent les Juifs. L’article propose un modèle pour enseigner dans les universités canadiennes l’art juif et des œuvres réalisées par des artistes juifs. Ce modèle permet aux étudiants de mieux comprendre le contexte culturel dans lequel ces œuvres sont créées, notamment *Dust*, œuvre de l’israélo-canadienne Sylvia Safdie. Mots-clés : diversité, inclusivité, art juif, Sylvia Safdie.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".