The Game FAVR: A Framework for the Analysis of Visual Representation in Video Games
Bibliographic record
Abstract
This paper lays out a unified framework of the ergodic animage, the rule-based and interactiondriven part of visual representation in video games. It is the end product of a three-year research project conducted by the INTEGRAE team, and is divided into three parts. Part 1 contextualizes the research on graphics and visuality within game studies, notably through the opposition between fiction and rules and the difficulties in finding common vocabulary to discuss key visual concepts such as perspective and point of view. Part 2 discusses a number of visual traditions through which we frame video game graphics (film, animation, art history, graphical projection and technical drawing), highlighting their relevance and shortcomings in addressing the long history of video games and the very different paradigms of 2D and 3D graphics. Part 3 presents the Game FAVR, a model that allows any game’s visual representation to be described and discussed through a common frame and vocabulary. The framework is presented in an accessible manner and is organized as a toolkit, with sample case studies, templates, and a flowchart for using the FAVR provided as an annex, so that researchers and students can immediately start using it.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".