Cross-dressing, Male Intimacy and the Violence of Transgression in Third Reich Photography
Bibliographic record
Abstract
Abstract This co-authored article draws upon two distinct genres of photography, police mugshots and amateur soldier snapshots, to illustrate the value of queer visual culture methodologies for how to think about the visualization of violence, masculinity and desire. Building upon two vastly different depictions of male to female cross-dressing, one produced between 1934 and 1938 by the Berlin police and Gestapo of a transvestite and the other produced between 1940 and 1944 by cross-dressing Wehrmacht soldiers behind the lines, we argue that in searching for evidence of intact identities, we overlook important ambiguities. We first show how different photographic traditions have framed gender and sexual non-conformity in the historical record and then go deeper into an image analysis. Drawing upon cross-dressing as a polyvalent performance of masculinity, as play, camp and an identity category, we explore how photographic sources help us better appreciate the multiple and sometimes coexisting layers of feminized masculinity at work during the Third Reich. By queering Nazi history and reading the police mugshots and amateur Wehrmacht photographs within and beyond their visual frames, we point out the limits of reducing every image of cross-dressing to an expression of an inchoate gay or trans identity and argue for analysis that embraces the multiple layers of histories gathered around visual sources.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".