Forgiving and Forgetting: A Post-Holocaust Dialogue on the Possibility of Healing
Bibliographic record
Abstract
At the end of this century there are so many occasions, so many residues of the most violent of times, that challenge the very idea of forgiveness—residues personal, political, social, and cultural. The harms are vast and yet close to home: alcoholism takes its toll on relationships, divorce undermines love, parental harshness and abuse create generations of problems for offspring, addictions of every sort turn humans into caged spirits. Additional and even greater challenges include infidelity, breaking public promises, political power plays, torture, genocidal slaughtering of races and tribes, civil and cultural wars, ancient enmities—Northern Ireland, Bosnia, the Tutsis and Hutus, the Shiite and Suni Moslems, the settlers and African immigrants in South Africa, indigenous populations against the dominant culture. The open violence and rapaciousness of human enmity can be viewed now in the displacement of masses of people in Kosovo. Said the U.N. High Commissioner for Refugees, Sadako Ogata, about the Kosovo crisis: “It is frightening … that this century, as in its darkest hours, should end with the mass deportation of innocent people.”
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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 | 0.030 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.142 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.023 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 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".