Bibliographic record
Abstract
Found throughout the Transatlantic World, fugitive slave advertisements demonstrate the ubiquity of African resistance to slavery. Besides noting things like names, accents, languages, and skills, they also recounted details which disclosed the regional origins and ethnicities of the runaways. Although detailed analysis of fugitive slave advertisements have been produced since the 1970s, Canadian Slavery has been conspicuously absent from such studies. This chapter exposes and challenges Canada’s overwhelming absence from Slavery Studies more generally, recognizing the ways that the Underground Railroad has been enshrined in the national curriculum and popular imagination to erase the colonial violence of Euro-Canadian settler histories. Challenging the erasure of Canadian Slavery, fugitive slave advertisement are analyzed to reveal the complex heterogeneity of the enslaved population of African descent. Focusing on Quebec from the moment of British conquest (1760), I argue that this heterogeneity was a hallmark of the enslaved population of Quebec, which was composed of African Canadian, African American, African Caribbean, African-born, and indigenous enslaved peoples. The chapter then poses directions for future research which can further explore the cultural, linguistic, spiritual, and social implications of this extraordinary diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".