Family Variables as Predictors of Self-Concept and Academic Achievement of Secondary School Students in Benue State, Nigeria
Bibliographic record
Abstract
OBJECTIVE: The study investigated family variables as predictors of self-concept and academic achievement of secondary school students in Benue state, Nigeria. METHODS: The study adopted a correlational research design. The sample of the study consisted of seven hundred and twenty (720) SS II students. The study research questions were analyzed using the Pearson product moment correlation coefficient, while the hypotheses were tested using regression analysis at 0.05 probability level. RESULTS: The findings revealed a strong positive relationship among family structure, self-concept and academic achievement of secondary schools students. It also revealed that family leadership style like Authoritative, Authoritarian, Permissive and Neglectful family leadership style had a strong relationship on secondary school students, self-concept and academic achievement. Again, it was discovered that family size had a strong relationship on secondary school students’ self-concept and academic achievement in Benue state, Nigeria. CONCLUSION: It is concluded that there exists a strong relationship between family structure and self-concept of secondary school students; family structure predicts students’ academic achievements; family size has a low positive relationship with self-concept of secondary school students; low relationship with student's academic achievement; there is a positive relationship between family leadership styles and self-concept of secondary school students.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".