“You Don’t Have A History”: Passion as the Counter-Narrative of Heritage, History, and Archives
Bibliographic record
Abstract
This paper describes my lifelong journey as a historian, heritage activist and librarian-archivist focused on recovering and sharing the history of Quebec and Canada’s English-speaking Black communities. This paper yet again contends that this community faces silence and invisibility. I share personal and professional examples of how I became passionate about Black history and I reveal the real-life consequences of its erasure. From this context, I provide detail on how this passion fueled decades of research, storytelling, and the building of collections and historical and archival materials. Over three decades ago, I responded with groundbreaking books: Blacks in Montreal 1628-1986: An Urban Demography (1989), and The Road to Now: A History of Blacks in Montreal (1997). Since that time, I have written extensively on the need to reshape Canada’s narrative so that it may embrace the diversity we boast about. I share how my efforts to address our cultural ignorance have led to the recent creation of the ABC’s of Canadian Black History Kit. Finally, my article concludes with an underscoring of how that very passion was rooted in my life’s mission to counter my own invisibility in Canada’s narrative.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.062 | 0.083 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".