The Game FAVR: A Framework for the Analysis of Visual Representation in Video Games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it