Selfies of Ill Health: Online Autopathographic Photography and the Dramaturgy of the Everyday
Bibliographic record
Abstract
This article offers a preliminary investigation into what I term “selfies of ill health” and traces the expansion of the autopathographic genre in visual media from professional art photography to the vernacular selfie in recent years. In this context, the word autopathography is used to describe self-representational practices that offer a first-person perspective on experiences of illness or hospitalization. I first situate the genre by identifying several typologies of selfies of ill health, including diagnostic selfies, cautionary selfies, and treatment impact selfies. I then focus on the forms of identity performance that selfies, and selfies of ill health in particular, deploy. I argue that the performative qualities of certain selfies of ill health overlap with salient characteristics of autopathographic practice in the arts. Using Karolyn Gehrig’s #HospitalGlam series as a case study, I examine how autopathographic selfies can also construct a politicized dramaturgy of the lived body, notably by enabling individuals like Gehrig to “come out” as being invisibly ill. I conclude that the dramaturgical thrust of such autopathographic imagery is to convey both the centrality of medical experiences in subjects’ lives and their specific desire to be publicly identified as persons living with illness. In light of this, although selfies of ill health may have opened up new avenues for autopathographic practice thanks to the affordances of social media, their communicative intents remain consistent with those of earlier forms of autopathographic photography.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".