Bibliographic record
Abstract
In this article, I consider ‘skin portraiture’: a mode of representation that privileges quasi-anonymous, fragmented, magnified and anatomized images of skin. I argue that this mode of representation permits a heightened awareness of embodied experiences such as reflexivity, empathy and relationality. Expanding understandings of difference through its engagement with haptic imagery and visuality, skin portraiture reorients the boundaries between ‘I’/‘not I’ and subject/object – often through touch – and challenges the cultural commitment to traditional notions of bodily autonomy. By doing so, skin portraiture functions as an antagonistic form of portraiture; that is, as a kind of anti-portraiture that pushes the genre into an expanded visual field and, at times, beyond representation. Exploring the skin-as-technology metaphor, I show that bio art skin portraiture creates chimeric skins through tissue culturing practices, permitting bodies to become radically relational. Bio art skin portraits celebrate the genetic and cellular differences between bodies through a visible collapse of epidermal boundaries, which engenders a hyper-haptic mode of seeing beyond the subject and her or his skin. Analysing the bio art of Jalia Essaïdi, ORLAN and Julia Reodica, and drawing on the work of Laura Marks and Erin Manning, this article explores the skin-as-technology metaphor in order to offer the arts and humanities an innovative understanding of contemporary embodiment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".