Curating Visual Archives of Sex
Bibliographic record
Abstract
Abstract In this roundtable, four curators of exhibitions showcasing sexual archives and histories—with a particular focus on queer and trans experiences—were asked to reflect on their experiences working as scholars and artists across a range of museum and gallery formats. The exhibitions referred to below were Bring Your Own Body: Transgender between Archives and Aesthetics, curated by Jeanne Vaccaro (discussant) with Stamatina Gregory at The Cooper Union, New York, in 2015 and Haverford College, Pennsylvania, in 2016; Odarodle: An imaginary their_story of naturepeoples, 1535–2017, curated by Ashkan Sepahvand (discussant) at the Schwules Museum (Gay Museum) in Berlin, Germany, in 2017; Queer, curated by Ted Gott, Angela Hesson, Myles Russell-Cook, Meg Slater (discussant), and Pip Wallis at the National Gallery of Victoria, Melbourne, Australia, in 2022; and TransTrans: Transatlantic Transgender Histories, curated by Alex Bakker, Rainer Herrn, Michael Thomas Taylor, and Annette F. Timm (discussant) at the Schwules Museum in Berlin, Germany, in 2019–20, adapting an earlier exhibition shown at the University of Calgary, Canada, in 2016.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".