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Record W3175131209

The Challenges of Globalization and the Use of Children’s Literature in Achieving Cultural Literacy in Nigeria

2010· article· en· W3175131209 on OpenAlexvenueno aff
Njemanze Queen Ugochi

Bibliographic record

VenueStudies in literature and language · 2010
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsGlobalizationDiligenceHonestyValue (mathematics)LiteracyCultural globalizationRelevance (law)SociologyQuality (philosophy)Social sciencePsychologyPolitical sciencePedagogySocial psychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

In the past, Africans attached great value to the transmission of their values and norms to their younger generation. This was done through oral traditions, such as story telling, songs, lullabies, riddles, plays and more. This emphasises continuity and cultural preservation .Cultural transmission aims at producing a generation who will sustain the communities’ values like honesty, perseverance, diligence, good community and brotherly relationship. Children’s Literature refers to quality books for children ranging from birth to adolescence; while globalization is the process of increased inter dependence and integration of cultures and values among nations, This paper thus, discusses the use of children’s literature as an instrument of achieving national integration in the era of globalization. It also examines the relevance of children’s literature to the society and asserts that children’s literature could be used to promote and sustain the national heritage. It will serve as a voice to forestall loss of our values. It proffers suggestions and concludes, .by stating how to achieve these aims. Keywords: Children’s literature; Globalization and Cultural Literacy

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)0.007
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0090.010
Scholarly communication0.0100.009
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.338
Teacher spread0.318 · 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
Published2010
Admission routes1
Has abstractyes

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