Bibliographic record
Abstract
Research on diversity and inclusion in game studies has focused so far on issues related to gender, race, and sexuality. Issues related to language have generally been left unaddressed. Nevertheless, the language we use to express ourselves has a major influence on the way the knowledge we create is perceived and transmitted (or not) worldwide, and thus also generates situations of privilege and exclusion. Building on the concepts of linguistic monopoly and linguicism (i.e., discrimination based on language), this chapter proposes a critical reflection on the dominance of English language in game studies. It argues that linguicism occurs in this discipline and in academia more broadly (1) through the anglicization of research and the need for non-native English speakers (NNES) to publish and present their work in English in order to be read, cited, and recognized as contributors to game studies; and (2) through a gatekeeping process, especially within high-ranking journals, that makes it harder for NNES to publish their work due to the absence of linguistic support from publishing institutions and the emphasis these institutions put on so-called “good” English. These inequalities are reinforced by the myth that English is a neutral language that serves everyone equally well and leads to cohesion and community. This chapter ultimately aims to bridge the gap between research on linguistic monopoly in academia and the discipline of game studies, seeking to critically reflect on how game studies have been built and are still being built around English language.
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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 | 0.001 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".