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Record W4290975669 · doi:10.5430/wjel.v12n6p315

The Value of Multiculturalism and Language in Children's Literature: A Critical Study

2022· article· en· W4290975669 on OpenAlexvenueno aff
Baker Mohammad Bani-Khair, Ziyad Khalifah Alkhalifah, Abdullah A. Jaradat

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismCultural pluralismPluralism (philosophy)Multicultural educationSociologyCultural diversityHumanityIdeologyValue (mathematics)Diversity (politics)Openness to experienceEpistemologyRepresentation (politics)Social sciencePedagogyLinguisticsPsychologySocial psychologyPolitical scienceAnthropologyLawPhilosophy

Abstract

fetched live from OpenAlex

Celebrating language diversity, cultural pluralism, and multiculturalism has long been seen as a sign of cultural openness and awareness in children's literature. Many good examples on cultural pluralism are seen in children’s books that celebrate the value of humanity as a universal concept rather than only a representation of national identity. The paper emphasizes the invitation to call upon pluralism as well as the cultural diversity which constitutes a new vision in children’s literature. Despite the fact that children’s literature has developed over an extended period of time, one has to understand the role that cultural diversity and its representation in children’s literature have in developing education and classrooms’ curriculum. However, as the paper showed us, multicultural children’s literature is still growing as many scholars and writers began to realize the importance of multicultural education in terms of social, ideological, and linguistic aspects.

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)0.011
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0220.040
Scholarly communication0.0130.012
Open science0.0020.010
Research integrity0.0030.005
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 head0.008
GPT teacher head0.301
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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