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Girls Building Androids and Robots

2017· book-chapter· en· W2754485697 on OpenAlexaboutno aff
Maya Götz, Diana Iulia Nastasia, Judith Johnson

Bibliographic record

VenueAdvances in computational intelligence and robotics book series · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsConversationCompetence (human resources)Television seriesPsychologyPolitical scienceMedia studiesSociologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

There is still a considerable degree of catching up to do in regards to fostering gender equality within areas of STEM (Science, Technology, Engineering, and Math). Children's leading medium, television, could offer role models of girls with competence in STEM areas, but unfortunately television programs often miss this chance. The children's television series Annedroids is a notable exception. This chapter provides insight into how children can be educated about gender equality in STEM with the aid of gender-sensitive media programs such as Annedroids. The chapter examines data from a reception study which was conducted under the leadership of the International Central Institute for Youth and Educational Television (IZI) with 6- to 12-year-old children in the United States and Canada (N = 301). The research is enhanced by a conversation between Dr. Maya Götz, Head of IZI; Dr. Diana Nastasia, a contributor to the IZI research; and J. J. Johnson, the program's creator.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.392
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.324
Teacher spread0.293 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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