Bibliographic record
Abstract
Abstract This chapter discusses the aesthetics and ethics of staging exile and migration as one of the focus points in the political theater of today. It argues that political theater has the power to engage with the strategies of critical countermapping of migration. Using affect, immersion, and embodiment, it can rehumanize migrants, the underclass, and national abjects. It can also stage the uniqueness of individual journeys within the impersonality of the global movements. Political theater can give voice to an asylum seeker and can return dignity to a victim. Telling stories about migration and confronting the bodies of the performers-refugees with the bodies of the spectators–their hosts, it can turn a nameless migrant into a proper individual, someone who possesses their personal history, memory, agency, and identity. Bringing stories of migration to the homes of those people who practice mixophobia, political theater can make the stranger relatable. The play The Jungle (2017), written by Joe Murphy and Joe Robertson, directed by Stephen Daldry and Justin Martin for the Good Chance Theatre, and presented by the National Theatre and the Young Vic in London, serves this chapter as its primary example of how political theater can educate its audiences about the other and help them realize that this other is already within us.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".