Bibliographic record
Abstract
The Montreal Life Stories project emerged out of the vibrant community of practice that formed in and around the Centre for Oral History and Digital Storytelling at Concordia University.The seven-year project brought together a diverse group of university faculty, students, artists, educators, and community members to record the life stories of those displaced by war, genocide, and other human rights violations.Almost five hundred people were interviewed in multisession interviews and hundreds more participated in parallel participatory media and performance-based workshops.In the early days, I remember feeling something of an imposter as I am not a specialist in genocide studies.Hitherto, my research had focused primarily on how people experience and understand transformative economic change.My years of interviewing experience had brought me into conversation with hundreds of people but never a genocide survivor.Obviously, this is no longer the case.In the beginning, as the project's primary investigator, I was intensely aware of the limits of my knowledge.This feeling faded as time passed and my experience deepened.I brought my own expertise and perspective to the project.Oral History at the Crossroads is just one of the many outcomes of our seven-year conversation.The pages that follow owe their existence to the individual and collective efforts of all those who served as team members and agreed to be interviewed.It was a privilege to work alongside such an extraordinary group of people.I especially want to thank the other members of the project's coordinating committee who met monthly over the past seven years.They are
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.398 | 0.215 |
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".