Bibliographic record
Abstract
Nina Bunjevac’s graphic memoir, Fatherland (2014), tells the story of her father, Peter Bunjevac, who died when she was a year old while preparing a bomb to attack the Yugoslavian Consulate in Toronto as part of his activities as a member of the Serbian terrorist group ‘Freedom for the Serbian Fatherland’. This man is depicted as a distant, elusive father, through an account that is marked by gaps and aporias, and which is based on historical and newspaper accounts, portrait photographs, and testimonies told primarily by Nina’s mother and maternal grandmother. In this article, I take Fatherland as a case study to explore the perpetrator portrayals that are enabled by the comics form. I investigate how the figure of the perpetrator becomes structured through the perspective of a daughter who did not know him, and I demonstrate that the technique of braiding, bird-related imagery, and visual as well as textual circles become instrumental in foregrounding inter-generational traumatic bonds that seem to have triggered abusive and violent behaviours. Furthermore, I argue that the narrative’s oscillation between the macro-level of the nation and the micro-level of the family, on the one hand, and between public and private histories, on the other, enriches and complicates the graphic display of this otherwise elusive, ‘monstrous’ perpetrator. In so doing, I showcase the value of graphic perpetrator narratives in facilitating more nuanced understandings of the figure of the terrorist, particularly in the post-09/11 context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".