White Archives, Black Fragments: Problems and Possibilities in Telling the Lives of Enslaved Black People in the Maritimes
Bibliographic record
Abstract
Slavery is the most neglected aspect of pre-Confederation Canadian history. Yet traces and examples of slavery are imprinted in various documents throughout the Maritimes, Upper Canada, and Quebec. It is not surprising that slavery played a part in Canadian history, but it is startling that it has not received widespread attention from the general Canadian public and historians alike. Just five years ago, Ken Donovan noted that, although various artists, writers, directors, and historians have worked on slavery, it “is not a significant part of the Canadian historical narrative.” Studying slavery in Canada, as opposed to, say, escaped American slaves, “goes against the dominant image of Canada as a land of freedom.” The focus, understandably, when discussing African Canadian history has been on the Underground Railroad, the Black Loyalists, and other groups who found freedom under the British flag. Yet thousands of black people were enslaved in colonial Canada between the seventeenth and early nineteenth centuries (1605–1820). We must recognize the significance of slavery to the history of Canada even if there were no large-scale slave plantations or major staple products like cotton or tobacco. One way to recognize slavery is to examine biographical sketches of enslaved people in the Maritimes and other parts of Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".