Bibliographic record
Abstract
This article was prompted by the return of the feral child—a figure shaped by the infantilizing racial logics that animate(d) the project of empire—in conservative responses to the August 2011 “riots” in and around London. I argue that the casting of young people, especially young people of colour, as animalistic and wild fleshes out a neoliberal plot that narrates the “nanny” state as failed and beseeching authoritarian correction. To consider how this works and, more importantly, to re-turn the trope that equates hoodies with “wild beasts,” I read Joe Cornish’s science fiction/horror/comedy film, Attack the Block (UK, 2011), as a comment on the invasiveness of neoliberal metaphors. Cornish’s “too black” aliens dramatize the force with which the trope of the feral child invades and inhabits the bodies of racialized young people, turning them toward the reproduction of a convenient narrative and, at the same time, exposing them to injury, violence and death. By staging this invasion as in-process and contested rather than accomplished, ATB opens up the possibility of telling different stories about young black Britons in urban England. But I also attend, here, to the film’s imaginative limits, which are signalled by the persistent abjection of the only “female” alien in the story. To multiply the “convivial” future possibilities to which ATB gestures, I meditate on the implications of doubling down on alienness: a turning and turning again that might usher in alien new worlds.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.028 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".