Understanding Game Art Practice Beyond Technical Expertise: A Qualitative Study
Bibliographic record
Abstract
Although they contribute a great deal to what players see on the screen, there is a marked absence in the literature of direct studies of artists working in digital game development. This is why we stress the need to understand game art practice in real-world industry settings, and particularly professional practice beyond technical expertise. Referring to design theory (i.e., Schön and others), professional artistry/knowledge in game art practice is understood as design-like process and skills. This qualitative study is based on ethnographic results from our doctoral research that have not yet been published in English. Cross-synthesis of three case studies is reported (each case comprises one experienced artist shadowed in a Montréal indie game studio during preproduction, between 2016-2018). Findings give insights on the ‘design-like’ professional artistry of game art practice, in terms of complex situations, common point of view on game experience and development, and core skillset. Referring to the theme of the conference, the findings point to ways of stimulating the growth of game art students’ design-like professional artistry in terms of interdisciplinary and sustainable collaboration in game development. Specifically, they strengthen the importance of humanistic skills (e.g. collaborative, ethical), combined with creative and technical ones, in professional practice of game art.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".