Internet Addiction: Relationship with Perceived Freedom in Leisure, Perception of Boredom and Sensation Seeking
Bibliographic record
Abstract
This study aimed to examine the university student’s internet addiction, perceived freedom in leisure, leisure boredom, and sensation seeking level with regard to gender and physical activity participation, and to investigate the relationship between internet addiction, perceived freedom in leisure, leisure boredom and sensation seeking. The participants who were chosen using a convenience sampling method filled the “Short Form of Young’s Internet Addiction Test” (YIAT-SF), Perceived Freedom in Leisure Scale (PFLS), “Leisure Boredom Scale (LBS), and “Sensation Seeking Scale” (SSS). T-test, MANOVA, ANOVA and correlation analysis were used to analyze the data. T-test results indicated there were no significant differences in the mean scores of “YIAT-SF” with respect to gender (p> 0.05). However, analysis revealed significant differences in the mean scores of “YIAT-SF” with regard to not regularly physical activity participation. There were significant differences in the mean scores of “PFLS” in favor of men participants and regularly physical activity participants (p<0.05). Gender and regularly physical activity participation were significant of "LBS” (p<0.01) in favor of women participants (p<0.05). Similarly, gender were significant of “SSS” (p<0.01) on the all sub-dimensions in favor of men participants (p<0.05). However, there were no significant differences in the mean scores of regularly physical activity participation.
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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 | 0.003 |
| 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 | 0.000 |
| 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".