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Record W4239211932 · doi:10.3138/cras.42.1.82

Suffer the Children: National Crisis, Affective Collectivity, and the Sexualized Child

2012· article· en· W4239211932 on OpenAlexvenueno aff
Susan Knabe

Bibliographic record

VenueCanadian Review of American Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsThe ImaginaryPoliticsInnocenceSociologyGender studiesVirginity testVulnerability (computing)IdeologyForegroundingPsychoanalysisPsychologyLiteratureArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract: This article takes up the explicit and implicit political deployment of children in relation to two recent enactments of childhood innocence and vulnerability: Dateline NBC's wildly popular series, To Catch a Predator, where men who think they are meeting an underage child for sex are caught on camera, exposed, and arrested; and the phenomenon of Purity Balls, a growing practice within fundamentalist Christian communities, in which a daughter pledges her virginity to her father during a yearly father-daughter formal ball. The article situates these particular examples within the political and ideological utilization of children that has been increasingly apparent within the American political landscape in the latter quarter of the twentieth century. It also draws on the theoretical work by Lee Edelman, Lauren Berlant, and Peter Coviello in order to explore the political and cultural implications of the way that the trope of childhood vulnerability circulates to reiterate a particular kind of heteronormative, patriarchal social imaginary, what Edelman identifies as reproductive futurism, and to anticipate the constitutive effects of vicarious trauma for imagining the nation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.013
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.029
GPT teacher head0.352
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2012
Admission routes1
Has abstractyes

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