Journal of Information Systems Education Volume 11(3-4) Some Observations On Internet Addiction Disorder Research
Bibliographic record
Abstract
Internet addiction is a contemporary problem brought about by easy access to computers and online information. Individuals addicted to the Internet can develop many types of disorders. In extreme cases, persons addicted to the Internet may be destructive to themselves, their families, and their place of employment. Corporate executives need to have a better understanding of Internet addiction because employees with Internet addiction can be highly counter- productive as well as cause other legal problems. This study examines research trends in the area of Internet addiction and provides management implications for policy development and planning. Specifically, this study identifies the leading researchers, institutions, specialization, and information dissemination outlets for Internet addiction research in the last quarter of the lOth Century to the present. This study should be of interest to educators at academic institutions, students interested in institutions offering Internet addiction courses and programs, and researchers specializing in online addiction studies. Clinical psychologists, behavioral counselors, psychiatrists, clergy, and addiction therapists will find the results of this study useful. In particular, corporate attorneys dealing with addiction cases, human resource specialists seeking rehabilitation facilities for addicted employees, health related policy makers, computing consultants, and risk assessors of insurance companies will find the results of this study to be valuable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".