Sparks of Light: Survivor Narratives Reflected Through the Lens of Irving Greenberg’s Theology
Bibliographic record
Abstract
In the future, when no first-person witnesses to the Shoah are left to tell their stories, we will be left with their written, audio, and video narratives. Those who will be left to bear witness for them can tell their stories from a purely historical viewpoint. However, this thesis adds a new option — a theological dimension through which to read and further appreciate their stories. It argues that the theology of Irving Greenberg is well suited to express the theology implicit in the lives of many Shoah survivors, providing an opportunity to reflect theologically on their experiences both during and after the Shoah. The thesis explores Greenberg’s distinct postwar model that expresses concern for the theological and socio-political implications of the Shoah, addressing themes of morality, faith, religion and interfaith dialogue. The second part of the thesis examines the narratives of three Canadian Shoah survivors through the lens of Greenberg’s theology. The first, Stefan Carter, is a secular Jew who survived the Warsaw Ghetto and makes his home in Winnipeg. The second, Pinchas Gutter, is a Majdanek/Buchenwald survivor who was from a family of Ger Hasidim. Gutter lives in Toronto and has become well known due to his participation in cutting-edge exhibits and videos. The third, Robbie “Romek” Waisman, is a Buchenwald survivor, lives in Vancouver and is an Honorary Witness to the Truth and Reconciliation Commission of Canada.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.047 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".