The case for graphic counter-memorials in the comics of Joe Sacco, Art Spiegelman, and Brian Wood and Riccardo Burchielli
Bibliographic record
Abstract
My dissertation considers a group of contemporary comics about war by Joe Sacco, Art Spiegelman, and Brian Wood and Riccardo Burchielli, as examples of a larger genre I call the graphic counter-memorial. Graphic counter-memorial comics address history, memory, and trauma as they depict the political, violent, and collective aspects of war and social conflict. I argue that the particular comics I study in this dissertation, which mingle fiction and non-fiction and autobiography as well as journalism, follow the tradition of the counter-monuments described by James E. Young. Studying commemorative practices and counter-monuments in the 1980s, Young notes a generation of German artists who resist traditional forms of memorialization by upending the traditional monument structure in monument form. Young looks at the methods, aims, and aesthetics these artists use to investigate and problematize practices that establish singular historical narratives. Like these works of public art, the graphic counter-memorial asks the reader to question ‘official history,’ authenticity, and the objectivity typically associated with non-fiction and reporting. I argue that what these comics offer is an opportunity to re-examine comics that incorporate real and familiar social and historical events and wars. Comics allow creators to visually and textually overlap perspectives and time. Graphic counter-memorials harness the comic medium’s potential to refuse fixed narratives of history by emphasizing a sense of incompleteness in their representation of trauma, memory, and war. This makes possible a more complex and rich way to engage with Western society’s relationship to the past, and in particular, a more complex way of engaging with collective memory and war. Their modes of mediating history produce political intervention through both form and content.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 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".