Bibliographic record
Abstract
This dataset contains a full, professionally conducted interview with Spanish artist Javier García Oliva, who has contributed to major Capcom franchises including Dead Rising and Puzzle Fighter. Conducted by Andrés Domenech Alcaide for CoolJapan.es in February 2018, the interview explores García’s career path, his early artistic influences, workflow in 2D and 3D digital art, experiences working in international studios, and insights into the video game industry. The interview highlights his personal journey from self-taught experimentation with RPG Maker and M.U.G.E.N., through formal studies in Fine Arts and Graphic Design, to professional work at Capcom Vancouver. García discusses his inspirations, tools and techniques, studio workflow, collaboration with international teams, and practical advice for aspiring video game artists. This dataset also includes high-quality images taken during the interview, depicting García in his workspace, interacting with iconic Capcom characters, and working on 3D models. All content is published under a CC BY 4.0 license, making it fully citable and usable for academic research, teaching, and professional reference in game design, digital art, and media studies. By providing first-hand insights into a professional video game artist’s creative process and industry experience, this interview serves as a valuable resource for scholars, students, and professionals in game design, animation, digital art, and interactive media.Originally published in Spanish on CoolJapan.es
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".