Meaning in motion: A kinesemiotic approach to videogame analysis in Street Fighter V
Bibliographic record
Abstract
Videogames are a semiotically rich medium, capable of utilizing virtually all modes of human expression (Ensslin 2012; Hawreliak 2018). Furthermore, they rely heavily on visual signification practices and have historically been the leading driver of advances in graphical technologies. This makes them ideal objects of study when examining animation trends, techniques, and representational practices. In this paper, we propose a novel methodological approach to analyzing animation in videogames, drawing on principles of Multimodality (Kress and van Leeuwen 2021; Jewitt 2009; Bateman, Wildfeuer, and Hiippala 2017) and Kinesemiotics (Maiorani 2021). As a variegated research area that focuses on communication across media, Multimodality provides a rich theoretical background, especially in the domain of Kinesemiotics, which focuses on movement-based communication in natural, hybrid, and digital environments. Drawing on the Functional Grammar of Dance (Maiorani 2017, 2021; Maiorani and Liu 2022) and its implementation in manual and digital annotation, the area of Kinesemiotics has expanded beyond the study of dance discourse and towards performance analysis in general, thus proving a flexible and adaptable approach. This method has considerable potential for systematically analyzing choreographed movements performed by characters in videogame environments. To demonstrate the value of this approach, we present a systematic analysis of movement in the popular fighting game Street Fighter V: Championship Edition (Capcom 2020), focusing on how gender-based stereotypes are coded in character movements. By comparing the same movements between female and male characters (e.g., a kick), we demonstrate how the game encodes gender norms through animation, i.e., how a move is conveyed as ‘feminine’ or ‘masculine.’ We conclude the paper by examining how representations on-screen are influenced by animation practices themselves, including motion capture direction. This research presents a novel methodological approach for investigating animation generally and has the potential to illuminate unconscious bias in the animation industry.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".