Bibliographic record
Abstract
\n\t\t\t\t\tIn Remembering Genocide an international group of scholars draw on current research from a range of disciplines to explore how communities throughout the world remember genocide. Whether coming to terms with atrocities committed in Namibia and Rwanda, Australia, Canada, the Punjab, Armenia, Cambodia and during the Holocaust, those seeking to remember genocide are confronted with numerous challenges. Survivors grapple with the possibility, or even the desirability, of recalling painful memories. Societies where genocide has been perpetrated find it difficult to engage with an uncomfortable historical legacy.Still, to forget genocide, as this volume edited by Nigel Eltringham and Pam Maclean shows, is not an option. To do so reinforces the vulnerability of groups whose very existence remains in jeopardy and denies them the possibility of bringing perpetrators to justice. Contributors discuss how genocide is represented in media including literature, memorial books, film and audiovisual testimony. Debates surrounding the role museums and monuments play in constructing and transmitting memory are highlighted. Finally, authors engage with controversies arising from attempts to mobilise and manipulate memory in the service of reconciliation, compensation and transitional justice.\n\t\t\t\t
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".