Graphic Resilience: Illness, Identity, and Empowerment in Neelam Kumar’s To Cancer, with Love: A Graphic Novel
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.The depiction of illness in comics has garnered increasing scholarly attention since the formalization of graphic medicine. This interdisciplinary field reimagines the affective and sociocultural dimensions of illness, disability, and healthcare through the unique affordances of the comics medium. Unlike typical prose-based illness narratives, graphic narratives utilize a multimodal language that enables a layered depiction of medical experiences, allowing readers to engage with illness not just as a clinical condition but as a deeply personal and socially embedded phenomenon. The visual grammar of comics further enables the representation of affective states such as pain, fear, and hope through metaphor, panel transitions, and spatial arrangements, often surpassing the expressive limitations of prose. Despite its growing prominence, graphic medicine remains predominantly Eurocentric, with scholarly discourse largely centered on works produced in the United States, Canada, and the United Kingdom. Graphic narratives emerging from non-Western contexts, particularly India, remain significantly underexplored. Addressing this critical gap, the present interview article focuses on To Cancer, with Love: A Graphic Novel (2017) by Neelam Kumar—India’s first graphic pathography—and reads it in conjunction with its prose counterpart, To Cancer, with Love: My Journey of Joy (2015). Through an email interview, Kumar reflects on the artistic and narrative strategies she employs to disrupt stereotypes, cultivate resilience, and depict self-care in the context of illness. The article further explores how the intersection of graphic medicine and Indian cancer culture foregrounds the need to address not only medical treatment but also the psychosocial dimensions of survivorship, gendered experiences of care, and the culturally specific framing of illness and recovery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".