Hidden Stories, Toxic Stories, Healing Stories: The Power of Narrative in Peace and Reconciliation
Bibliographic record
Abstract
Research on narrative is more than simply listening to (more or less) nice stories. There are stories that are hidden between the lines; these need to be noticed and retrieved. There are stories that can be toxic to be exposed to; these need to be coped with and conceived. But there may be stories that have a healing quality, too—stories that can contribute to peace and reconciliation. These three possible qualities of narratives are the focus of the following paper, which was delivered in October 2008, at the launch of the Centre for Interdisciplinary Research on Narrative at St. Thomas University in Fredericton, New Brunswick, Canada. The lecture was based on his interdisciplinary research project Geschichte und Erinnerung [History and Memory, www.geschichte-erinnerung.de] in which interviews with Nazi followers, bystanders, and perpetrators were conducted and analysed. Marks presented one of the key findings of this research—shame—and its effect on what the interviewees recounted, as well as its relevance for National Socialism and present-day German society.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.070 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".