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Record W4320518213 · doi:10.3726/b20054

Histories of Children’s Television Around the World

2023· book· en· W4320518213 on OpenAlexaboutno aff
Yuval Gozansky

Bibliographic record

VenuePeter Lang Verlag eBooks · 2023
Typebook
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsChinaDiversity (politics)PoliticsPolitical scienceMedia studiesIdeologyPublic relationsSociologyLaw

Abstract

fetched live from OpenAlex

The book puts together for the first time valuable updated information that looks at children’s television from its early days up to the current digital age, with its vast digital media offerings and availability. It offers new insights about a central children’s media culture and focuses on non-Anglo-American television histories. Thus, readers interested in understanding past to present, local and global processes in children’s television, would be able to find it in one book. Scholars, students, and professionals working in the field of children, as well as everyone concerned with children’s culture will find a great diversity of knowledge about the cultural, social, political, and economic contexts of programs with which they and their children have grown up. This edited book is based on a collective effort of researchers and professionals dedicated to compiling the stories of children’s television around the world. With 12 national chapters, the book includes historical accounts of children’s television from the following countries: Australia, Brazil, Canada, China, Ecuador, Germany, India, Israel, Italy, Kenia, Netherlands, and the United States. It provides an exploration of each individual country, revealing striking similarities and differences which are discussed in depth in the final chapter. Looking at the global field through local eyes––its main texts and active players (broadcasters, producers, and creators, as well as regulators and policy makers), their ideologies, financial prospects, and perceptions of childhood––offers a macro-level evaluation of an entire cultural field. This is a valuable picture, as it also provides a contextualized perspective for reflection in any micro-analysis of specific programs.

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0110.009
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.032
GPT teacher head0.286
Teacher spread0.255 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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