Modeling the pathways from antisocial media exposure to subjective well-being through school-based victimization in Nigeria
Bibliographic record
Abstract
The pervasive integration of digital media into daily life is reshaping how individuals encounter and internalize harmful contents. Unrestricted access exposes students to emotionally disruptive materials, including depictions of violence, substance use, and harassment, raising concerns about its impact on well-being. This study examines a serial mediation model linking antisocial media exposure to subjective well-being (SWB) through school-based victimization as sequential pathways. Using data from 326 high school students in Nigeria, we applied partial least squares structural equation modeling to test hypothesized relationships. Results indicate that antisocial media exposure was not directly associated with SWB but significantly predicted experiences of teacher and peer victimization. Peer victimization mediated the relationship between antisocial media exposure and SWB (β = -0.023, 95% CI: [-0.054, -0.004], p < 0.05). Furthermore, antisocial media exposure increased the likelihood of teacher victimization, which facilitated peer victimization, ultimately compromising SWB (β = -0.030, 95% CI: [-0.058, -0.011], p < 0.05). Effects varied by gender and academic level, underscoring intersectional risks linked to media exposure. Findings highlight the need for targeted interventions addressing both teacher and peer victimization in resource-constrained educational contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".