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Record W4377093845 · doi:10.3138/jeunesse-2022-0025

Performing Childhood: Maddie Ziegler and the Presentation of Young Girls in Video Clips

2023· article· en· W4377093845 on OpenAlexvenueno aff
Sophia Mehrbrey

Bibliographic record

VenueJeunesse Young People Texts Cultures · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)EntertainmentPopularityCLIPSDancePsychologyPopular cultureVisual artsArtLiteratureSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Since the 1980s, the production of video clips has been on the rise. Over the past couple of years, clips showing dance choreographies, often embedded within a rather conceptual fictional framework, have greatly increased in popularity. Although the musicians are usually adults, it is not uncommon to cast children and young adults to perform in the videos. At the same time, multiple reality TV formats in which children and young adults compete performing dance choreographies to famous pop songs have become a popular form of entertainment. The American TV series Dance Moms is a prominent example of this phenomenon. One of the best-known young artists in the field is Maddie Ziegler, who made her debut in this show and subsequently appeared in several music videos by the singer Sia. Maddie, born in 2002, was eleven years old when she first performed in a video clip. Her career is emblematic of the presentation of young girls in a popular culture and media that is dominated by the deliberate play with expectations of age and the blurring of categorical distinctions. Drawing on Maddie Ziegler as an example, the following article provides a close analysis of the presentation of young girls in video clips and reality shows. Special attention is given to the atemporal image of adolescence in these adult reconstructions, which, instead of celebrating the rebellious potential of youth, oscillate between the angelic outer appearance of the child and the serious demeanour of the adult.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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.

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

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