Forced Entertainment? Gamified Surveillance in Theatre Conspiracy’s Foreign Radical
Bibliographic record
Abstract
The nature of surveillance is changing. It is becoming gamified. This article charts a shift in thinking about surveillance culture, from the panopticon models advanced by Jeremy Bentham and Michel Foucault to current analyses of the increasingly participatory and gamified models of surveillance in the age of big data. That shift is played out literally in Theatre Conspiracy’s immersive play Foreign Radical, in which participants are led through a game environment in which they reveal aspects of their online behaviour and judge each other in a way that replicates the kinds of social sorting that take place in both social media and surveillance. The game leads them to deliberate on the case of an Iranian-Canadian man named Hesam, who stands accused of terrorism. As the case against Hesam comes to rest increasingly on his Middle Eastern and Islamic identity, the show reveals the role that racism and Islamophobia come to play in the interpretation of surveillance data. Although surveillance operates on a seemingly empirical basis, the stories it tells about people nevertheless remain fictions, assembled from statistical probability and speculation. In this way, surveillance adds to the sense that we live in a post-truth society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".