<i>Containing Childhood: Space and Identity in Children’s Literature</i> edited by Danielle Russell
Bibliographic record
Abstract
middle-class childhood innocence (112).Austin observes how artefacts reinforced the 'larger culture narrative of the decade that Black [female] bodies must be regulated and white [female] bodies educated' (112).In addition to summarizing anxieties about race and reproduction, this chapter analyses how monstrosity is linked to queerness, homosexuality, and 'queer' families.Austin claims that monster texts and cultural artefacts offer children symbolic methods to enact the cultural changes feared by parental/societal institutions.In Austin's conclusion, Monsters, Inc. is used to demonstrate that identification with the monstrous empowers childhood resistance, suggesting that adults need to stop fearing children so children can contribute openly to culture.Sara Austin's book argues that monstrous fiction teaches that 'no matter how much force a ruling body exerts on its populace, children's opinions will not mirror those of adults' (149).Societal and adult attempts to support the status quo will be undermined by young people as they subvert attempts to police them by mobilising popular culture and economic agency to enact the changes 'the monstrous' opens to them.The notion of the monstrous has been enormously influential in shaping society's ideas about what is normal and typical, but it also opens space for new ways to understand gender, sexuality, and social belonging.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".