Group Dynamic Concepts in Social Studies as Correlates of Moral Values and National Unity in Nigeria
Bibliographic record
Abstract
Nigeria is a multilingual and multicultural nation which is characterized by ethno-religious crises and insurgencies. The introduction of an active approach to teaching would ensure effective education and socialization for transformation in Nigeria. Effective teaching of group dynamic concepts (GDC) is relevant because of the diversity of students in our schools today. GDC are selected themes which could be used to help students from diverse racial, cultural, ethnic and language groups to experience unity through academic success. While academic knowledge and skills are essential, students must also develop positive attitude and skills necessary to interact positively in our diverse nation. This study, therefore, examined group dynamism as correlate of moral values and national unity in Ogun state, Nigeria. Two null hypotheses were generated and tested at 0.05 level of significance. The study adopted a quasi experimental design. A 30 item achievement test was administered on 150 junior secondary school (JSS) students randomly selected from five secondary schools in the south-west region of Nigeria. Data were analyzed using descriptive and inferential statistics. The Pearson product moment correlation and Scheffe Post hoc tests were used to determine the source of significant main effect where observed. The findings of this study revealed that effective teaching of GDC could help to inculcate the desired moral values in students and this could translate into national unity in and beyond Nigeria.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".