Bibliographic record
Abstract
This article examines several genres of role-playing games in terms of their procedural logics of racial management as an attempt to understand how game logics can express varying and often contentious ways of enacting “diversity.” It argues that games themselves can help answer one of the most persistent questions about games today: “how do we make games more diverse?” We proceed by defining the racial logics—the “diversity rules”—structuring the Mass Effect series (BioWare, 2007–), Genshin Impact (miHoYo, 2020), and Divinity: Original Sin 2 (Larian Studios, 2017). These games respectively place the player in the role of multicultural manager, racial empath, and divine avatar. These games show us the many logics, strategies, and appropriations that can occur when diversity itself is treated not as a complex process toward building social justice, but as an obtainable asset, and as the sole win condition in making and selling a game. Attending to these racial logics can open paths to new disciplinary directions in game studies by pushing beyond established domestic boundaries, liberal multiculturalist definitions of diversity, and ultimately into revealing our regional attitudes and particular ways of defining and practicing “diversity.”
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".