Background Checks: Disentangling Class, Race, and Gender in CRPG Character Creators
Bibliographic record
Abstract
Character backgrounds are one of many elements players use to customize their protagonists in fantasy computer role-playing games. By documenting the narrative trappings, mechanical benefits, and hierarchical availability of character backgrounds in Arcanum: Of Steamworks and Magick Obscura (2001) and Dragon Age: Origins (2009), this paper considers how real-world socioeconomic class markers and racial stereotypes have been repeatedly associated with fictitious races such as orcs, dwarves, and elves. Class is an understudied axis of identity in media studies and this research scrutinizes how developers construct socioeconomic class, particularly through character-creator interfaces. We begin by building a theoretical repertoire for studying identity in digital game interfaces while also scrutinizing long-established discourses of race and gender in the fantasy genre. We then analyze the hierarchies embedded in both games’ character creators, connecting them with broader gameplay and narrative themes and contextualizing them in established media stereotypes and existing scholarship.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".