Cyber spirituality II: virtual reality and spiritual exploration
Bibliographic record
Abstract
Video gaming, whether online or via gaming systems, is a highly popular pastime for children and youth. Cartoon Network’s New Generations surveys in the Philippines and India indicate that, of young people aged 7–14 with Internet access, 68% of Filipino children regularly participate in multi-player online games ( Cartoon Network 2007 ) and 53% of Indian children identify multi-player or individual gaming as their favourite online activity ( Demott 2010 ). An eight-year-old Nielson study of online gaming in Europe found that 12–24-year-olds are twice as likely to play online games as other age groups ( Nielson 2003 ) and a more recent study in China reports that 8.1 million Chinese aged 25 years and under comprise one half the total number of Internet users in that country and cite online gaming as a top activity (Youth Mesh 2008 ). Among South Australian children, video game play comprises 19% of their multimedia time ( Olds, Ridley, and Dollman 2006 ) and a Ministry of Education and Training survey in Vietnam ‘showed 70 to 76 percent of primary school children play online games on weekdays’ ( CNN 2010 ). A Canadian study found that boys under 20 are more likely (80%) to play online games than girls in the same age group (20%) and that most play between 12 and 24 hours per week ( Gladwell and Currie 2009 ). The most recent study released in the USA reports that children of 11–14 years old on average spend the most time playing video games each day (85 minutes), but all children aged 8–18 average more than an hour of daily gaming activity (Rideout, Foehr, and Roberts 2010).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".