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Record W4387134140 · doi:10.3366/ircl.2023.0532

<i>Containing Childhood: Space and Identity in Children’s Literature</i> edited by Danielle Russell

2023· article· en· W4387134140 on OpenAlexaff
Mia Arciniegas

Bibliographic record

VenueInternational Research in Children s Literature · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIdentity (music)Space (punctuation)PsychoanalysisSociologyPsychologyArtPhilosophyAestheticsLinguistics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.322
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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