Bibliographic record
Abstract
A dynamic dialogue of poetry and art that reimagines the ancient, biblical concept of sacrifice. Winner of the 2022 Gerald Lampert Memorial Award presented by the League of Canadian Poets A collaboration between poet Alisha Kaplan and artist Tobi Aaron Kahn, Qorbanot -the Hebrew word for "sacrificial offerings"-explores the concept of sacrifice, offering a new vision of an ancient practice. A dynamic dialogue of text and image, the book is a poetic and visual exegesis on Leviticus, a visceral and psychological exploration of ritual offerings, and a conversation about how notions of sacrifice continue to resonate in the twenty-first century. Both from Holocaust survivor families, Kaplan and Kahn deal extensively with the Holocaust in their work. Here, the modes of poetry and art express the complexity of belief, the reverberations of trauma, and the significance of ritual. In the poems, the speaker, offspring of burnt offerings, searches for meaning in her grandparents' experiences and in the long tradition of Orthodox Judaism in which she was raised. Kahn's paintings on handmade paper, drawn from decades of his career as an artist, have not previously been exhibited or published. They reflect his quest to distill a legacy of trauma and loss into enduring memory. With a foreword by James E. Young and essays by Ezra Cappell, Lori Hope Lefkovitz, and Sasha Pimentel, the book presents new directions for thinking about what sacrifice means in religious, social, and personal contexts, and harkens back to foundational traditions, challenging them in reimagined and artistic ways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.617 | 0.308 |
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".