Look Behind You! Playing with Sexual Surveillance in You Must Be 18 or Older to Enter and how do you Do It?
Bibliographic record
Abstract
In this article, I consider two indie videogames, You Must Be 18 or Older to Enter (Seemingly Pointless 2017) and how do you Do It? (Freeman et al. 2014), that share an interest in the affective impact of parental surveillance and discipline on childhood sexual exploration. Using close playing as my method, I argue that the videogames reveal the perils of surveillance and its pleasures. Drawing on assemblage theory, I demonstrate the contingency of videogames’ affective impact on players and the world and the—sometimes contradictory—potentials that surveillance produces as part of a sexual assemblage. Sometimes, using surveillance as a game mechanic amplifies sexual affect and pleasure and can thus be conceptualized as an example of “flirting” with surveillance. At other times, players orient themselves toward the videogames as the archetypical “parent”—finding pleasure in “catching” a videogame about sexual exploration and attempting to discipline it. Finally, drawing on Kathryn Bond Stockton (2004, 2009, 2017) and José Esteban Muñoz (2019), I argue that, by allowing players to relive childhood sexual exploration as adults, You Must Be 18 or Older to Enter and how do you Do It? provide players with the opportunity to become a playful child, to loop back through time and re-explore sexual discovery, and thus shape nuanced critiques of the way surveillance shapes sexual possibilities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".