A Dance of Shadows and Fires: Conceptual and Practical Challenges of Intergenerational Healing after Mass Atrocity
Bibliographic record
Abstract
The legacy of mass atrocity—including colonialism, slavery or specific manifestations such as apartheid—continue long after their demise. Applying a temporal intergenerational lens adds complications. We argue that mass atrocity creates for subsequent generations a deep psychological rupture akin to witnessing past atrocities. This creates a moral liability in the present. Healing is a process dependent on the authenticity (evident in discourse and action) with which we address contemporary problems. A further overriding task is to open social and political space for divergent voices. Acknowledgement of mass atrocity requires more than one-off events or institutional responses (the grand apology, the truth commission). Rather, acknowledgement has to become a lived social, cultural and political reality. Without this acknowledgement, healing, either collectively or individually, is stymied. Healing after mass atrocity is as much about political action (addressing inequalities and racism) as an act of re-imaging created through constant and contested re-writing.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.127 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".