‘I Would Pretend to be a Dude’: NBA 2K Gamers’ Motivations, Use of WNBA Features, and Experiences With Harassment
Bibliographic record
Abstract
When NBA 2K20 was released in 2019, it was the first time Women’s National Basketball Association players were included in the game. This study’s lead author conducted ethnographic fieldwork and 17 semi-structured interviews with American and Canadian NBA 2K players to explore how the presence or absence of female avatars influence male players’ perceptions of who plays the game, and in what ways NBA 2K serves as a platform for fostering or hindering female gamers’ participation in competitive gaming spaces. Since the game only had male avatars, participants often assumed their opponents were male. Female gamers said it wasn’t unheard of for NBA 2K players to exit the game when they realize there was a female player competing against them. In order to avoid this and other forms of harassment, women said they tried to change their voices in game chats to avoid being identified as women. In many cases, a male ally acted as a shield from such online harassment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".