“The Pleasures in Being Seen”: An Interview with Dani Lessnau, Led by Drs. Stéfy McKnight and Julia Chan
Bibliographic record
Abstract
When conceptualizing the call for this special issue, one artist came to our (Julia and Stéfy’s) minds: Dani Lessnau. Her work straddles complexities of surveillance, voyeurism, desire, and female pleasure. In particular, we want to highlight Lessnau’s provocative performance photography series extimité created in 2017. Using a pinhole camera inserted into her vagina, she photographs the sexual intimacies and relationships with her partners. We ask more broadly, what does it mean to use surveillance as a method of pleasure? And, how can artists subvert or appropriate the surveillant gaze in ways that disrupt heteropatriarchy? We are grateful to have had the opportunity to explore these tensions and questions with the artist herself in this interview. Thank you, Dani, for engaging with us in this topic.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.021 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".