Gaining New Insights into Professional Knowledge in Digital Game Art by Taking a Design Perspective
Bibliographic record
Abstract
Although artists contribute a great deal to what digital game players see on the screen, there is a marked absence in the literature of direct studies of artists working in digital game development. We stress the need to understand these artists’ professional knowledge in a rich and contextualized manner, and beyond technical expertise. In this paper, we describe the design process carried out by an experienced technical artist during game preproduction. We report findings gained through ethnography/shadowing at the Montreal-based Red Barrels studio. We refer to pragmatist and constructivist theories of professional design practice to make sense of its reflective, collaborative, situated, and transactional aspects. This paper draws conclusions on three ideas: (1) the benefits of using design theory to examine design-like reflective skills in game art practice; (2) the utility of qualitative methods to construct a thorough, holistic, and contextualized understanding of professional practice, and (3) how a richer, more elaborate understanding of ‘design’ in game development points to a need for further research on the sociocultural aspects of game experience design.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".