The Benefits of Banding: Overcoming Barriers to CommunityParticipation Among Magic: The Gathering Players
Bibliographic record
Abstract
Many game communities, including those who primarily play Magic: The Gathering (MTG), struggle with different kinds of toxicity, often directed towards players of minority gender identities. To help understand how these players deal with the barriers they face, we conducted a two-phase mixed-methods study. After surveying 324 MTG players and interviewing 14 of them, we found such players encountered barriers such as male-dominated environments, stereotyping and underestimation and developed strategies of community support, including personal adaptation based on previous systemic familiarity and alternate formats to persist and succeed. The research highlights economic barriers, cultural and social barriers, along with knowledge and experience gaps. Important themes include recognizing cultural norms, overcoming stereotyping, engaging selectively, and building inclusive playgroups, resilience and adaptability. We believe these strategies imply a broader need for intentional inclusivity practices and support mechanisms within gaming communities to foster a more equitable and representative gaming public.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".