Bibliographic record
Abstract
In 1995, psychologist Kimberly Young of the University of Pittsburgh coined the phrase Internet addiction (IA). Six years later, Young has opened the world‚s only ”virtual clinic” to help people deal with their online problems. Young, who bills herself as ”the world‚s first cyberpsychologist,” runs the Center for On-Line Addiction (www .netaddiction.com). It includes checklists of warning signs, tips on how to deal with problems and a self-administered assessment test. There is even a ”cyberwidows” test aimed at spouses and tests to determine whether you are addicted to cybersex or online gambling. ”Impairment to real-life relationships appears to be the number-one problem caused by Internet addiction,” says Young. ”Internet addicts gradually spend less time with real people in their lives in exchange for solitary time in front of a computer.” Young‚s site says IA covers a variety of behaviours and impulse-control problems, including cybersex, cyber-relationships, online gambling and trading, excessive Web surfing and general addiction to computer games or programming. Once identified as an online addict, people can surf over to Young‚s virtual clinic, where they can sign up for email, online ”chat” or telephone counselling. Have your credit card handy. A patient history is taken — online, of course — and then service commences. The cost runs from US$15 US for a single email response ”session” to US$210 for 180 minutes worth of chat or telephone counselling. All major credit cards are accepted. Although the online clinic does not offer any claims about its success rates, it does highlight the number of times Young‚s work has been featured in the mass media, in venues ranging from the New York Times and Wall Street Journal to the BBC and Good Morning America.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.301 | 0.153 |
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".