Bibliographic record
Abstract
As the gaming industry and culture continue to shift and mold toward consumers, developers and companies are in a constant race to learn to adapt and remain relevant. With masterful crafting of marketing strategies, increasing capital investment in game development, and even closer attention towards consumer relationships, Riot Games, a prominent company within the gaming industry, boasts to be a strong case study of the complex intersectional, material, and cultural dynamics of play as situated practices. Boasting a robust gaming culture with almost unrivaled business success, Riot is quickly becoming a multibillion-dollar business (Jarrett 2021). With its perceived success within the gaming community, it is important unpack the logic of success that construe Riot as a model to learn from despite the world of compliant raised against it. In doing so, we can use these logics of success and the world of complaint to complicate the façade of success that is fabricated through uneven relations of power establishing the hegemony of play. Since the advent of video games, scholars have long studied the hegemony of play, diving deep into the exploration of the social impact of technology, positioning of video games in society, and the power structures that video game industries uphold (Fron et al. 2007; Gray 2020). The tendrils of hegemony of play extend far and wide, finding its traces in racial capitalism, alienating minority groups, exploitative game production, and so much more. By delving deeper into the logic of success in conjunction with the hegemony of play, one can acquire the tools to critically analyze and critique the criteria by which Riot’s success is measured among different communities. The two main criteria of interest explored in this piece include financial success and success in crafting reputation. Juxtaposed against these two criteria of success, this paper will then dive deep into the world of complaint expressed against Riot. Building on Sara Ahmed’s theorization of complaint that explores how complaints are often made behind closed doors and how people who raise complaints frequently have their doors shut on them (Ahmed 2021), this paper proceeds to offer a systematic analysis on the power structures embedded in the logic of success that perpetuate the hegemony of play. One may ask, why is it important to raise questions and critique the logic of success holding up Riot Games’ status? We must pull away focus on surface-level success and attend to complaint to understand how this hegemony of play is upheld—if one wants to disrupt the hegemony, we must care for complaints. Engaging in a series of textual analysis, this paper will draw from trade journals, news articles, and discussion forums in the contexts of not only financial and reputational success, but also the world of complaint as Riot is no stranger to controversy, being subject to gender-based discrimination lawsuits and culture of sexism (Liao 2022).
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".