De-picturing John A. Macdonald: Opportunities and Challenges of Representing Canada’s Past with Graphic History
Bibliographic record
Abstract
This article explores the historiographical and methodological opportunities and challenges of graphic history to represent, interpret, and interrogate Canada’s past. Graphic history is a research-creation approach that combines word and picture to produce illustrated texts and comic book-style narratives. While I address important critiques about academic rigour, pedagogical value, and practical viability, I argue that graphic history has much potential to offer historians. By broadening our understanding of scholarly work, graphic histories can be accessible sources for wider audiences, critical resources for teaching and learning, and/or imaginative methods for engaging with historiographical issues. After examining the theories and practices of graphic history, I illustrate a graphic-text essay on the contested images of John A. Macdonald. Pictures of the first prime minister are well known to most Canadians in photograph, caricature, and statue, but his legacy has come under greater academic and public scrutiny, particularly regarding policies towards Indigenous peoples. I focus on Macdonald because debates over his commemoration are relevant to the ways in which historians represent and confront complicated pasts. I use related debates over statue removal and anxieties about erasure of history to explore deeper historiographic questions about representation, truth, presentism, and perspective. I argue that a graphic history approach is a medium for deconstructing, or, as I call it, de-picturing, a one-dimensional, dominant image of Macdonald on a pedestal, exhibited in bronze.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.032 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".