Puncture Wounds.: Sensing Gender Non-Conformity Stigma in the Portrait
Bibliographic record
Abstract
My current research prepares ethnographic portraits ofvisual activists who create portraits of local trans communities of Berlin, Johannesburg, and Toronto.This chapter reflects on how negative affects surrounding gender non-conformity in conjunction with race and age becomes worked through in drawn and photographic portraiture.The chapter outlines how the project is situated within trans and critical race studies, explains the starting points within portraiture theory, and, along the way, introduces a selection of images from participating artists such as Del LaGrace Volcano, Elisha Lim, and Zanele Muholi.The chapter will also analyze ways in which the artists'practices of collaborative portraiture negotiate the puncture wounds of stigmas, particularly around gender non-conformity in conjunction with racialization.Transgender is an umbrella identity term for those who may wish to pursue a social and/or physical change ofgender and, consequently, who may not fit within a society's strictures on how men and women should look or act.In short, because transgender individuals do not align with the standard expectations of the gender assigned to them at birth, they can become victims of gender-normative stigma and violence.Social stigma refers to modes of public exclusion, such as visual markers and negative traits attributed by the greater society (Goffman 1963).Hence, the visual field can become a battleground for legitimating or discriminating against gender expressions.Scholars such as Jay Prosser (1998), Benjamin Singer (2006), and Jamison Green (2004) have all explicated the imporrance of visual politics for trans bodies, ranging from the use of personal photogra- ph¡ to depictions in medicine, to recognition in everyday life respectively.By way of outlining the project "VitalArt:Transgender Portraiture asVisual Activismr" this chapter will explore the question of how the representation of stigma in visual art can provide insight into the experiences of transgen- der discrimination and its negotiation through images.First, to clarifii specific terms: in Tiansgender History, Susan Stryker uses transgender to refer to "people who move away from the gender they were assigned at birth, people who cross over (trans-) the boundaries con- structed by their culture to define and contain that gender" (2008, l).Tians (or trans*) is now commonly used as a general category for the multiplicity of these identities and practices.Trans avoids the direct use of medical terminology such as transsexual, which many find pathologizing of their gen- der difference."Gender variant" and "gender nonconforming" are other community-developed terms for people who move away from the gender 33 Puncture Wounds: Sensing Stigmata ¡n the Gender NonConforming Portrait
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".