Condition Verified: On Photography, Trans Visibility, and Legacies of the Clinic
Bibliographic record
Abstract
We approach this paper with a shared investment in historical and contemporary representations of trans and gender non-conforming people, and our individual research in the archives of early US Gender Clinics. Together, we consider what is at stake—or what might be possible—when we connect legacies of photography used as diagnostic tools in gender clinics with snapshots of early, community-based gatherings, and the presence of trans people in contemporary art. From the archives of Robert J. Stoller and photos of Casa Susanna, to the collaborative photography of Zackary Drucker and Amos Mac, and the biometric data art-theory experiments of Zach Blas, we engage a series of image-based projects, which animate underlying questions and socio-political debates about the politics of visuality, and visibility’s impact on trans and gender non-conforming people. Moreover, we argue that rhetorical strategies of proof—from conditions verified in clinics to shared existence through photography—are tethered to, and thus trapped by, the logics and discipline of legibility and re-institutionalization.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.069 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".