Relationship between Prejudice, Social Media Addiction, Empathy and Trust
Bibliographic record
Abstract
The study examined the relationship between prejudice, social media addiction, empathy and trust among teenage boys and girls. The study also investigated if there were any differences in prejudice, social media addiction, empathy and trust between teenage boys and girls. The sample consisted of 200 students out of which 100 were male students and 100 were female students. The data was collected through online forms as well as physical copies of the questionnaires distributed in schools. The students were administered Social Dominance Orientation Scale by Pratto et al (1994), Bergen Social Media Addiction Scale by Andreassen (2012), Toronto Empathy Questionnaire by Spreng et al (2009) and General Trust Scale by Yamagishi, T. & Yamagishi, M. (1994). Pearson correlation coefficient was used to investigate the relationships between prejudice and social media addiction, prejudice and empathy, prejudice and trust, social media addiction and trust, social media addiction and empathy and empathy and trust. Independent samples t-test was used to examine the gender differences in prejudice, social media addiction, empathy and trust. The analysis revealed that there were significant gender differences in prejudice and empathy among teenage boys and girls, with boys having higher prejudice and girls having higher empathy. There were no significant gender differences in social media addiction and trust among teenage boys and girls. There was a significant negative correlation between prejudice and empathy in teenagers, both boys and girls. There were no significant relationships between prejudice and social media addiction, prejudice and trust, social media addiction and trust, social media addiction and empathy and empathy and trust among teenagers, both boys and girls.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".