Oral History as Creative Practice at Concordia University’s Centre for Oral History and Digital Storytelling
Bibliographic record
Abstract
Oral history is a field of research that is uniquely positioned to thrive at a time of intense public and scholarly interest in sharing personal stories. Unlike ethnography, oral history has not found an institutional home in North American universities given its often fraught relationship to the history discipline. History is typically grounded in distance—the more the better; whereas oral history is based on the idea of closing distance by learning with the communities we study. Yet oral history is thriving in Canadian universities in the in-between spaces of collaborative cross-disciplinary projects and research centres. After introducing readers to the situation in Canada, the article explores the ways that oral history and storytelling have come together at Concordia University’s Centre for Oral History and Digital Storytelling (COHDS). Oral history is a creative practice, and one of its great strengths is its openness to a diversity of approaches. This can be seen in the range of research-creation projects undertaken by oral historians at Concordia in the classroom and in our communities. Montreal Life Stories was the most ambitious of these projects, recording the life stories of 500 genocide survivors and integrating their stories in a range of public outcomes such as theatre plays, online digital stories, art installations, radio programming, documentary and animated film, and a museum exhibition. The article ends with consideration of the online Living Archives of Rwandan Genocide Exiles and Survivors as well as the new digital tools under development.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.024 |
| Scholarly communication | 0.024 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".