Bibliographic record
Abstract
How can bitter enemies who have inflicted unspeakable acts of cruelty on each other live together in peace? At a time in history when most organized violence consists of civil wars and when nations resort to genocidal policies, when horrendous numbers of civilians have been murdered, raped, or expelled from their homes, this book explores the possibility of forgiveness. The contributors to this book draw upon the insights of history, political science, philosophy, and psychology to examine the trauma left in the wake of such actions, using, as examples, numerous case studies from the Holocaust, Russia, Cambodia, Guatemala, South Africa, and even Canada. They consider the fundamental psychological and philosophical issues that have to be confronted, offer insights about measures that can be taken to facilitate healing, and summarize what has been learned from previous struggles. Dilemmas of Reconciliation is a pioneering effort that explores the extraordinary challenges that must be faced in the aftermath of genocide or barbarous civil wars. How these challenges of reconciliation are faced and resolved will affect not only the victims’ ability to go on with their lives but will impact regional stability and, ultimately, world peace.
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 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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".