Promoting Ethnic and Religious Diversity for the Nigerian School Children: A Preliminary Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".