A Study on Effective Revenue Model for Virtual World - Focusing on Payment Method of In-Game Assets in Social Virtual World
Bibliographic record
Abstract
The virtual world is a cyber gaming space where a player interacts with others through their avatars. It is now mainly developed in foreign countries including USA, Canada, and Europe and many experts say that the market capacity of virtual world will be continually expanded in worldwide. The virtual world will mostly be made such that a user accesses the world for free and pays for in-game activities. But it is hardly that we find the effective methodology of payment system for such virtual world due to its' short development history. In the case of Korea, various payment methods of selling in-game assets have been tested in online games. The paper propose an effective revenue model for social virtual world focused on selling in-game assets. The guideline of the proposed revenue model will be expected to contribute creating revenue focused on selling in-game assets, effectively for social virtual world.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".