Oral history and human rights: the archive and disability at Winnipeg's Oral History Centre
Bibliographic record
Abstract
Carlos Sosa, a member of the Manitoba League of Persons with Disabilities (MLPD) and the principal investigator in the MLPD Oral History Project, stated in a 2021 oral history interview that “it’s absolutely critical that we understand history, but we also learn from history to come up with a way forward where our most vulnerable are considered” (Sosa, 2021). Through this statement Sosa demonstrates the way that human rights and oral history intersect while highlighting the impact that research occurring at this intersection can have on human lives. During my Master of Human Rights (MHR) practicum placement at the Oral History Centre (OHC), I was able to interview Sosa as well as immerse myself in the theoretical and practical aspects of the production of oral history archives. Working at the intersection of human rights and oral history, I realized that although I previously felt that there was no space for someone with my backgrounds, interests, and experience in the field of human rights studies, there was in fact a gap between the oral history and human rights offering me an opportunity to insert myself and my competencies into an academic field that I am passionate about. I entered a Master of Human Rights program and the discipline of human rights studies anticipating being able to make use of my historical training. While this has occurred to some extent, I do not feel historical theory, specifically theory that informs the practice of oral history, has had its potential fully realized in the discipline of human rights. While at the OHC I witnessed the way that human rights are present in the collection, production, and accessibility of oral histories. This experience at the OHC reinforced my perceptions of the necessary relationship between oral history and human rights, even as it also showed me that the two approaches have not yet fully realized the potential arising from their intersection.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".