Democratizing Oral History: Sharing the Voices of Black and Indigenous Peoples
Bibliographic record
Abstract
Hear, Here is a critical public oral history project in the South of Horton (SoHo) neighborhood of London Ontario, Canada. The way it functions is that orange street signs with a phone number and the Hear, Here logo are placed in any location where a story (or stories) are told. When a visitor sees the sign and diles the number they hear a short (2 minute or less) oral history about the exact location in which they stand. If they stay on the line they can leave their own story about that location or any other location in the neighborhood. In this way the project becomes user generated, increasing the number of oral histories available for public consumption. The reason why we choose to SoHo neighborhood for Hear, Here is that it is a neighborhood in flux, undergoing partial gentrification, and going through various battles for meaning. The neighborhood was once a terminus point on the underground railroad, and is currently embroiled in a dispute over what to do with a former “Fugitive Slave Chapel:” white heritage workers from outside the neighborhood would like it to become a museum while a former Black minister of the church would like it to be a community gathering space. Similarly the grounds of the Old Victoria Hospital, which is in the midst of demolition, is under dispute. Should it be made into affordable housing units or expensive condo buildings with access to the Thames River? Hear, Here grapples with the inherent challenges of forging a democracy that gives voice to all of the inhabitants of a neighborhood regardless of race and economic status, while it seeks to amplify the voices of those who go typically unheard.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.039 | 0.030 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".