The Diagnosis of Online Game Addiction on Indonesian Adolescent Using Certainty Factor Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research discusses about online games which become ones of the most popular commodities, particularly among adolescents. It is suggested that they prefer spending time by playing online games to studying. Consequently, the phenomenon of online game addiction gets significant. The study aimed to explain and describe the use of Certainty Factor Method with expert system to diagnose online game addiction on adolescents. Its setting was Java island by involving some students as subject of the study. Survey method was employed to obtain the data while its instrument in terms of questionnaire was provided in Google forms and distributed to the students. An interactive qualitative approach was deployed to analyze the data combined with certainty factor method to show the level of game online addiction. The result shows from the sample of data which have analyzed addiction level toward online games is relatively moderate. Most of the users are adolescents between 12 and 15 years old and spend about 4 until 6 hours in a day. This condition becomes worse if there is no treatment for adolescents. Hence, there is a need of parents' control and awareness toward children's activity in playing online games since it renders negative impacts particularly on children's learning activity and motor development.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it