Positive and Negative Experiences, E-safety and Sharing with Others: Surfing the Internet and Social Networks and the Correlations Between Experiences, Self-image and Computer Skills Among Children and Adolescents
Bibliographic record
Abstract
In light of the many major changes in the lives of children and adolescents due to digital developments, this study sought to examine positive and negative experiences, e-safety and sharing with others while surfing the internet and especially social networks from the point of view of children and adolescents. The study also examined the correlation between these experiences, self-image and computer skills. Participating in this mixed-method study were 373 children and teenagers, who were divided into three age groups. The findings showed a positive correlation between self-image, the level of computer skills and the degree of internet use. The measure of self-esteem was found to correlate positively with the parameters of social networks surfing except for the parameter of negative experiences. Social networks and internet use among 16-18-year-olds was found to be higher than among younger children, with a rise in the number of teenagers’ negative experiences that corresponded to the rise in use. The adolescents also mentioned they had been exposed to violent content at a higher rate than the younger groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".