Repression of Obsessive-Compulsive Disorder Through Graphic Narrative in Ian William’s The Bad Doctor (2014)
Bibliographic record
Abstract
Comics is a sequential art that appeals to a diversified audience, a medium of reflection on culture, society, and history. Graphic literature is a discourse of dynamic interaction of graphics in literature, including literary comics, graphic novels, sequential art, juxtaposed images, and other dimensions of visual and printed images. A Graphic novel is the collaborative medium of the interaction of word and image, visual and verbal imagery to create numerous meanings and multiple interpretations. The term Graphic medicine was coined by Ian Williams who is a doctor, comic artist, and writer; He defined Graphic Medicine as the intersection of the medium of comics and the discourse of healthcare. Graphic medicine analyzes and interprets the medium of comics which serves as an innovative platform for disturbing, risky, and taboo ideas of illness. Ian Williams’s graphic novel The Bad Doctor (2014) is a verbal and visual illustration of Obsessive Compulsive Disorder (OCD). The protagonist Ian a general practitioner tries to cope with his OCD, his obsessions are comically and satirically represented through the artistic medium of graphic narrative and iconography of illness. Graphic Narrative of psychological illness like OCD by the sufferer of the illness gives a whole new perspective on the experience of psychological illness. Graphic narrative and iconographic representation of illness break down the traditional stereotypes of illness and give voice to the muted patient whose stories are unheard.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".