Oral History and Performance in the Aftermath of Organized Violence: \nAn Epistemological Contribution
Bibliographic record
Abstract
Oral History and Performance in the Aftermath of Organized Violence: \nAn Epistemological Contribution. \nLisa Ndejuru, Ph.D \nConcordia University, 2020 \n \nCan transdisciplinary, relational research-creation strategies open pathways to wellness, \nemancipation, and finding one’s voice in a post-colonial context of genocide, war, organized \nviolence, and exile? \n \nWhat are some affordances of performative inquiry, writing as inquiry, and other arts-based pedagogies and practices when applied to oral histories, memory work and sense-making? \n \nCan community dialogue, creative storytelling, deep listening help move toward healing in the aftermath of organized violence and traumatic loss, and exile? \n \nCan improvisational playback theatre with difficult stories appease the silences, and help defeat intergenerational transmission of the traumas of persecution and genocide and war. \n \nAs a child and grandchild of survivors of early anti-Tutsi injustices in Rwanda, as a wife and mother, I seek non-professionalized and non-medicalized solutions––accessible metaphors, tools and techniques––for use within my own afrodiasporic community setting, and beyond, as a new generation works through questions of memory, identity, change and transformation. \n \nMy learnings emerge out of a very personal perspective, reflecting more than 20 years of experience as an activist and organizer within the Rwandan diaspora in Canada, my work as a licensed mental health professional focused on the wellness of racialized minorities, my creative collaborations as a community artist, and the community-based research I have undertaken as a volunteer co-applicant of the 7-year SSHRC-funded CURA project Life Stories of Montrealers displaced by genocide, war and other human rights abuses, based at the Centre for Oral History and Digital Storytelling at Concordia University.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".