Identification of Internet Usage and Dependency Level of Physical Education and Sport Teaching Students
Bibliographic record
Abstract
The excessive internet usage that interrupts social relations, physical characteristics and mental conditions of individuals is called as internet addiction. At the previous studies, it is reported that university students are at risk of Internet addiction due to their uncontrolled lives without families and killing their time surfing the internet. The objective of this study is to identify the addiction level of university students’ internet usage within the framework of some variables. A total of 463 students, 194 girls and 269 boys, who study at different 7 Departments of Physical Education and Sport Teaching within School of Physical Education and Sports and Faculties of Sport Sciences, attended this study in 2016-2017 Academic Years. “Personal Information Form” is used to determine the internet addiction of students “Internet Addiction Scale” developed by Young (1998) and to determine the demographic features and the data regarding the internet usage. In this study, the risk average for internet addiction of students is determined 2.55. It has been observed that the individuals who addict the internet constitute 3.9% of my samples. It is confirmed that internet addiction of male students is considerably higher than female students. It is ascertained that students who have Internet and a social media account are under higher risk of internet addiction. It has been emerged that there is no significant relation between the age/ monthly income of the family and the internet addiction level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".