Introduction into the Virtual Olympic Games Framework for online communities
Bibliographic record
Abstract
OBJECTIVE: This paper presents the design of the Virtual Olympic Games Framework (VOGF), a computer application designated for athletics, health care, general well-being, nutrition and fitness, which offers multiple benefits for its participants. BACKGROUND: A special interest in starting the design of the framework was in exploring how people can connect and participate together using existing computer technologies (i.e. gaming consoles, exercise equipment with computer interfaces, devices of measuring health, speed, force and distance and Web 2.0 applications). METHOD: A stationary bike set-up offering information to users about their individual health and athletic performances has been considered as a starting model. CONCLUSIONS: While this model is in the design stage, some preliminary findings are encouraging, suggesting the potential for various fields: sports, medicine, theories of learning, technologies and cybercultural studies. First, this framework would allow participants to perform a variety of sports and improve their health. Second, this would involve creating an online environment able to store health information and sport performances correlated with accessing multi-media data and research about performing sports. Third, participants could share experiences with other athletes, coaches and researchers. Fourth, this framework also provides support for the research community in their future investigations.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".