The Comic Research Abstract: Graphic Medicine as Interdisciplinary Health Research (Example: Intergenerational Storytelling)
Bibliographic record
Abstract
This article explores the rise of comics-based research (CBR) as an innovative method for disseminating and translating academic findings to broader audiences. Rooted in the established use of comics in technical communication, CBR takes the unique strengths of graphic media-accessibility, multimodal engagement, and visual storytelling-to communicate complex concepts to diverse audiences, particularly in health-related disciplines. A recent development in this field is the comic research abstract, a concise, visually enriched alternative to traditional textual abstracts. By integrating clarity, brevity, and expressive visuals, this format enhances research accessibility and promotes interdisciplinary collaboration. Drawing on an example from the author's work on intergenerational storytelling, this article introduces the comic research abstract as a transformative interdisciplinary tool that bridges the arts, humanities, and health sciences. It highlights how this format translates research into advocacy-driven narratives, fostering inclusion, activism, and public engagement. By combining written and visual content, the comic research abstract underscores the potential of comics for advancing health humanities, arts-based academic communication, and inclusive scholarship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".