Disruptive exhibitionism - a performance methodology for surveillance art
Bibliographic record
Abstract
Recent years have seen an increase in work that critically names surveillance as a colonial logic, technology, and practice (see Browne 2015; Maynard 2017; Cahill 2019; Cahill 2021). To contribute to this turn, we propose ‘disruptive exhibitionism,’ a theoretical and methodological concept for surveillance performance art developed through the lenses of anti-colonialism, anti-racism, and queer positivity that center practices of care and pleasure as forms of resistance against surveillance structured by the violence and exploitation of white supremacist capitalist patriarchy. The aim of this article is to develop disruptive exhibitionism as a methodology for surveillance performance art and research-creation that offers a way for marginalized identities and bodies to engage with visibility, where public visibility may be fraught or even dangerous. Disruptive exhibitionism builds on Koskela’s (2004) important concept of ‘empowering exhibitionism,’ which suggests that individuals might resist surveillance by using surveillant technologies to self-represent and publicly ‘expose’ oneself voluntarily. Disruptive exhibitionism expands empowering exhibitionism to consider (a) those subjectivities and bodies whose public visibility has been erased and/or rendered dangerous and (b) how contemporary corporate culture, white feminism, and postfeminism have co-opted ‘empowerment’ (Banet-Weiser 2018; Beck 2021).
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".