Drawing on the Margins of History: English-Language Graphic Narratives in Canada
Bibliographic record
Abstract
This study analyzes the techniques that Canadian comics life writers develop to construct personal histories. I examine a broad selection of texts including graphic autobiography, biography, memoir, and diary in order to argue that writers and readers can, through these graphic narratives, engage with an eclectic and eccentric understanding of Canadian historical subjects. Contemporary Canadian comics are important for Canadian literature and life writing because they acknowledge the importance of contemporary urban and marginal subcultures and function as representations of people who occasionally experience economic scarcity. I focus on stories of “ordinary” people because their stories have often been excluded from accounts of Canadian public life and cultural history. Following the example of Barbara Godard, Heather Murray, and Roxanne Rimstead, I re-evaluate Canadian literatures by considering the importance of marginal literary products. Canadian comics authors rarely construct narratives about representative figures standing in place of and speaking for a broad community; instead, they create what Murray calls “history with a human face . . . the face of the daily, the ordinary” (“Literary History as Microhistory” 411). My research finds that contemporary Canadian graphic narratives create mundane personal histories using a medium that is inherently attuned to exaggeration and fragmentation. My reading of graphic narrative is based on “autographics,” a recent field of scholarship that analyzes the interactions between visual and verbal forms of communication in works of life writing. I draw on visual rhetorical studies and communication design in order to describe “the distinctive technology and aesthetics of life narrative that emerges in comics” (Whitlock 965). The medium of comics playfully manipulates the discourses of documentary evidence and testimonial authority. At the same time, it gives Canadian authors tools for depicting the experiences of ordinary individuals through a rich collection of emotional, sensorial, and perceptual information. Focusing on the work of such authors as Chester Brown, David Collier, Julie Doucet, Sarah Leavitt, and Seth, I suggest that Canadian comics authors exploit the unique formal properties of the medium of comics in order to interrogate dominant nationalist discourses. They also develop an alternative method for analyzing narratives about the past.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".