GAMING PLATFORMS AS CHAOTIC NEUTRAL?: TOXIC PERFORMANCE, COMMUNITY RESISTANCE, AND AGONISTIC POTENTIAL
Bibliographic record
Abstract
In the post-gamergate era, much has been written about the toxicity of online multiplayer video gamespaces. Yet, game scholars agree that the actual definition of the term ‘toxic’ is slippery. There is also consensus that toxicity is a highly context-dependent phenomenon reliant on the relation of players to one another but extending further to include the technical elements of the game (Canossa et al., 2021; Hilvert-Bruce & Neill, 2020; Kou, 2020; Kowert, 2020). Past scholarship in this area also illustrates that these spaces are deeply gendered and center masculine normativity (Cote, 2020; Gray, 2020; Ruberg, 2019; Shaw, 2015). Players from various positionalities may enter conflict when there is dissent over the definition and norms of the space. In these instances of conflict there is the potential for agonism (Laclau & Mouffe, 1985). We employed cultural probes in tandem with focus groups and interviews to better understand how players experience toxicity in online gaming spaces. Emerging from participants’ conversations, this paper explores performative behaviours which are emblematic of performing toxicity or ‘counterplay’. We propose three common instances of counterplay: antagonistic counterattack, when a player reciprocates or matches the toxic behaviour of an antagonist; ludic mithridatism, when a player develops a threshold for tolerating toxicity in a gamespace; and playful transgression, when a player or group of players performs counter-hegemonic identity-work.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".