"I Didn't See Anyone Who Looked Like Me": Gender and Racial Representation in Board Gaming
Bibliographic record
Abstract
Through a variety of mixed methods, this PhD dissertation asks whether a lack of diversity in the labour of board game design, and a lack of representation of women and non-binary, Black, Indigenous, Persons of Colour (BIPOC) in artwork of popular games acts as a potential barrier for board gaming cultures' potential growth, wider mainstream cultural adoption, and creates the conditions for exclusion and marginalization for those who identify as women, LGBTQiIA+, and BIPOC? The research conducted in support of this dissertation found that 92.6 percent of the labour of board game design was that of white-identified, male-identified creators in a sample of the top-ranked 400 board games on the global game repository, BoardGameGeek (BGG). This study further found that of the human representation found on the cover art of the boxes of the top 200 BGG games, images of men and/or boys represented 76.8 percent of the sample or 647 figures. Women and/or girls were represented 23.2 percent of the time or 195 figures in total compared to men. Only 17.5% of the human representation was that of Black, Indigenous, Persons of Colour (BIPOC) on the cover art of board games or 112 total figures, versus 528 images of white figures which represented 82.5 percent of the sample. Further, 320 respondents to an online survey shared that representation was a notable factor in their perceptions of, and behaviours within the hobby and industry, with 84.9 percent of the respondents indicating that diverse gender and racial representation was a problem in contemporary board games. A correlation was located in the representation of women, and BIPOC in game design and artwork and board game consumers play and purchase decision-making.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".