A Model of Using Digital Information Systems to Create Video Game Contexts: The Case of GPT Models and Its Effect
Bibliographic record
Abstract
The main goal of the study is to identify factors influencing the formation of video game content through GTP models, and based on these factors to formulate strategies for improving these processes.The object of the research is digital information systems for creating the context of video games.The scientific task of the study is the formation and selection of the most optimal strategy for adapting an information system under the influence of negative factors for video game developers.The research methodology includes the use of a combined approach, including hierarchy analysis, pairwise comparison of alternatives, expert analysis and the Delphi method.As a result of this combined analysis and innovative methods, we developed and selected the most optimal strategy for optimal use of GPT models in video games, which takes into account both technological opportunities and challenges.The study has limitations because the strategies were formed on the basis of a certain list of factors, which theoretically could lead to failure to take into account secondary factors and elements that may also influence the area being studied.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".