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Record W7135172688 · doi:10.5281/zenodo.19008748

Entrevista a Javier García, artista español en Capcom

2018· article· en· W7135172688 on OpenAlexaboutno aff
Andrés Domenech Alcaide

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt, Technology, and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsStudioPerspective (graphical)Video gameThe artsWorkflowProcess (computing)NarrativeVideo game development

Abstract

fetched live from OpenAlex

This interview with Javier García Oliva, conducted by Andrés Domenech Alcaide for CoolJapan.es, explores the career and creative process of a Spanish artist working at Capcom Vancouver, Canada. García shares his journey from studying Fine Arts and Graphic Design in Spain to joining one of the world’s most renowned video game companies, contributing to titles such as Dead Rising 4 and Puzzle Fighter. The discussion covers his experiences in both 2D and 3D art, his workflow with industry-standard tools like Maya, ZBrush, and Unreal Engine 4, and the dynamics of working in a large studio with international teams. García also reflects on the challenges of deadlines, inspiration sources, and career advice for aspiring video game artists. This interview provides a unique perspective on the professional development of a European artist within a major Japanese video game company.Originally published on CoolJapan.es in 2018.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1030.026

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 head0.031
GPT teacher head0.231
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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