The Little Black School House : Revealing the Histories of Canada's Segregated Schools—A Conversation with Sylvia Hamilton
Bibliographic record
Abstract
Segregated schools are a widely documented component of American history. Conversely, in Canada, provincially legislated segregation of Black Canadians has not been fully acknowledged. This historical amnesia raises numerous questions about the construction of Black experiences in both states. This interview examines Sylvia Hamilton's documentary The Little Black School House (2007), which explores the past as a means to contribute to the ongoing vitality of Black communities. Our discussion places this film within the historical context of legislated segregation in Canada and the United States, drawing attention to histories that have been largely absent within the dominant Canadian historical narrative. La ségrégation des écoles est un élément largement documenté de l'histoire américaine. Réciproquement, au Canada, la ségrégation des Canadiens de race noire réglementée par les provinces n'a pas été entièrement reconnue. Cette amnésie théorique soulève de nombreuses questions sur la construction des expériences des Noirs dans les deux États. La présente entrevue examine le documentaire de Sylvia Hamilton The Little Black School House (2007), qui explore le passé comme moyen de contribuer à la vitalité permanente des communautés noires. Notre discussion place ce film dans le contexte historique de la ségrégation légale au Canada et aux États-Unis, et attire l'attention sur les histoires qui ont été largement absentes dans le narratif historique canadien prédominant.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.069 | 0.027 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".