Book Review: Legal Institutions And Collective Memories, by Susanne Karstedt (ed)
Bibliographic record
Abstract
CERTAIN FUNDAMENTAL QUESTIONS have recently surfaced in the wake of systemic brutality and extreme atrocities that constitute war crimes, crimes against humanity, and gross violations of human rights.What is the most appropriate method for a society to punish the perpetrators?How does a society pay tribute to and provide reparations for individual victims?How does a society heal the collective trauma and devastation caused by brutality and atrocities?What are the.most effective steps to be taken to ensure that the past is not repeated?The role of legal institutions and legal processes and their engagement with and entanglement of individual and collective memories raise further difficult and complex questions.These questions have become staples for advocates and scholars in the human rights community, both at the local and international level, and several impressive texts dedicated to these issues have been produced.'Legal Institutions and Collective Memories, a welcome addition to the literature, contributes comprehensive historical, sociological, and legal perspectives to this important dialogue.Dedicated as a tribute to Maurice Halbwachs (1877-1945) and his coining of the concept of "collective memory,"' Legal Institutions serves to explore this notion in the wake of several experiments to address and redress war crimes, 1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".