Causes and Preventive Measures of Aggressive Behaviour among In-school Adolescents in Ilorin Metropolis: Stakeholders’ Perception
Bibliographic record
Abstract
Aggressive behaviour has been described to be an act engaged in to harm someone who is considered to be vulnerable. It has been observed to be on the increase among young people, most particularly, adolescents in our society. This study therefore examines the stakeholders’ perceived causes and preventive measures of aggressive behaviours among in-school adolescents in Ilorin Metropolis in Nigeria. A descriptive survey design was used for this study. Two hundred senior secondary school students and 100 teachers; making a total of 300 respondents, were selected for the study. A questionnaire was used to obtain data from the respondents. The instrument was validated by experts in the Department of Social Sciences Education. Two research questions were answered and two research hypotheses tested. Percentage and mean ratings were used to answer Research Questions One and Two, while an independent t-test was used to test the two formulated hypotheses at 0.05 level of significance. The findings of this study revealed that the stakeholders’ perceived causes of aggressive behaviour among in-school adolescents in Ilorin Metropolis was poor teacher-students’ interactions. Stakeholders perceived preventive measures to aggressive behaviour to be regular moral and religion teachings, and parental monitoring of what their children watch on social media. Based on the discussion of findings and the conclusions drawn, it was recommended that aggressive behaviour among in-school adolescents can be prevented by engaging in moral and religion teachings on aggressive behaviour.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".