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Record W3202051300 · doi:10.29173/iasl8308

Promoting Ethnic and Religious Diversity for the Nigerian School Children: A Preliminary Study

2021· article· en· W3202051300 on OpenAlexvenueno aff
Grace U. Onyebuchi

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupReligiosityRivalryDiversity (politics)Cultural diversityGender studiesSociologyPolitical sciencePsychologySocial psychologyAnthropology

Abstract

fetched live from OpenAlex

The aim of this study is to provide evidence on the school library as an important medium for promoting ethnic and religious diversity among the Nigerian school children. Though the issue of diversity continues to evolve and expand in the 21st century to include dimensions of race, ethnicity, gender, culture, abilities, sexual orientation, socio-economic status, age and religious preferences (Perrault & Mardis, 2015), the Nigerian nation is presently being troubled by the crisis related to ethnic and religious groups. The education system seems not to be doing much in encouraging coexistence among the citizens. The school library serves as a safe place for addressing these issues of ethnicity and religiosity among learners from diverse backgrounds in the Nigerian school system in which the school timetable is busy with a lot of passive learning activities. Even though ethnic and religious diversity should be addressed in a normal classroom environment, there is still a continuous rise in the lack of ethnic and religious coexistence in Nigerian community which has brought about religious rivalry and ethnic bigotry in the different communities of the nation (Akwanya, 2015; Ojo, 2016). This has continually led to a greater output of school children who are not ready to welcome other cultures and religious beliefs when they become adults; thus steering violence among other members of the community.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.045
GPT teacher head0.330
Teacher spread0.285 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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