‘That is the skin of my brother’: alterity, hybridity and media representations of facial transplantation
Bibliographic record
Abstract
In this paper, I explore the 2012 face transplant performed on US recipient Richard Norris and how it was represented by the media as a ‘makeover story’. Informed by press coverage from the date of the transplant to the present day, I examine a widely viewed and critically acclaimed investigative report that aired on CBS’s 60 Minutes entitled ‘My Brother’s Keeper’. Through a close reading of both its form and content, I claim that the report’s makeover story consists of four key themes: heroic medicine and miraculous science; appearance-based stigma and social alienation; appearance-based conformity and social assimilation; and subjective alterity and embodied hybridity. In doing so, I contend that the report’s themes contain the widespread ambivalence about facial transplantation by confirming prevailing assumptions about medical science and how it creates normal people who live good lives. That said, I also contend that the report’s themes complicate these assumptions by highlighting how facial transplantation invariably involves immediate encounters with otherness and corporeal interconnectedness. I conclude that the report’s makeover story—characterised as it is by the constraints of the before-and-after format—must be rethought and, ultimately, reworked if we wish to do justice to face transplant recipients.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".