Bibliographic record
Abstract
As transitional justice has grown as a field and as an international mandate in post-atrocity contexts over the past several decades, the memorialization of past atrocities through the construction of physical spaces of memory has increasingly been recognized as an essential aspect of this complex process. Often, these spaces of memory are touted not only as honoring past victims, but also as important tools for preventing future violence. To date, there has yet to emerge a clear way to measure exactly how sites of memory contribute to atrocity prevention. Can a site of memory really help prevent further acts of atrocity violence? If so, when and how are sites of memory a preventive force? This article describes the findings of three years of research into more than 400 memory sites around the world, with a focus on the programming and activities undertaken by various sites not only to engage with the past, but to respond to contemporary risks of large-scale, identity-based violence. This article posits that memory sites can be identified as preventive when they succeed at mitigating or eliminating any of the identifiable risk factors that lead to atrocity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".