Bibliographic record
Abstract
In this paper, we explore the relevance of graphic novels to understanding and responding to the complex nature of traumatic experiences. We argue that graphic narratives of trauma, which combine visual images and written text, significantly differ from biomedical and legal accounts by presenting the nuances of traumatic experiences that escape the conventions of written testimony. Building on the literature that integrates social justice concerns with visual methods and graphic medicine, we contend that graphic narratives effectively convey the complexities of traumatic experiences, including embodied experiences that are not always apparent, intelligible, or representable in written form, leading to greater social recognition of the dynamics and consequences of trauma. To illustrate this claim, we analyze Una’s Becoming Unbecoming (2015), a graphic novel that explores themes relating to trauma and social justice. Una relies on the graphic medium to explore the interconnections between personal and collective experiences of gender-based violence, and to show how physical embodied experience is central to her own experience of trauma. Graphic narratives like Becoming Unbecoming also offer a space for addressing the emotional, physical and financial costs of survivorship that usually are not available in legal written testimonies, potentially leading to better justice outcomes for trauma survivors in terms of social intelligibility and recognition, and access to social resources for healing.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".