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Record W310412390

A Study on Effective Revenue Model for Virtual World - Focusing on Payment Method of In-Game Assets in Social Virtual World

2008· article· en· W310412390 on OpenAlex
Ha-Jung Kim, Gyuhwan Oh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Korea Game Society · 2008
Typearticle
Languageen
FieldHealth Professions
TopicInnovation in Digital Healthcare Systems
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual worldRevenuePaymentVirtual economyRevenue modelBusinessMetaverseCommerceComputer scienceFinanceVirtual realityWorld Wide WebHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.151
GPT teacher head0.484
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it