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Record W2765352882 · doi:10.5539/jel.v7n1p174

Use of Children’s Popular Culture in Literacy Curricula: An Analysis of the Papers by Parry (2002) and Dickie and Shuker (2014) through the Lenses of “No Research Can Be Value-Laden”

2017· article· en· W2765352882 on OpenAlexvenueno aff
Fatma Aslantürk Altıntuğ, Emre Debreli

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPARRYCurriculumCreativityLiteracyPopular cultureSociologyPedagogyMedia literacyValue (mathematics)Critical literacyCritical thinkingPsychologyMathematics educationMedia studiesSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This paper focuses on the importance of children’s out-of-school experiences as their popular culture and discusses how such experiences contribute to their creativity and critical thinking. In addition to this, the paper also discusses the critical role of researchers’ values in terms of how values affect the design and process of research. From this standpoint, it analyses two research articles, namely as: “Popular culture, participation and progression in the literacy classroom”, by Parry (2014), and “Ben 10, superheroes and princesses: primary teachers’ views of popular culture and school literacy”, by Dickie and Shuker (2014). Throughout the analysis, the paper discusses how the values of the authors’ of these papers affected their selection of the research topics, as well as the process of research, by bringing out the importance of critical literacy in pre-school curricula.

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.011
Science and technology studies0.0040.007
Scholarly communication0.0070.006
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.347
Teacher spread0.298 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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