Cheerleaders/booth babes/<i>Halo</i>hoes: pro-gaming, gender and jobs for the boys
Bibliographic record
Abstract
In recent years, a ‘professional’ digital gaming industry has emerged in North America: this interconnected series of organisations and leagues host competitive gaming tournaments (often televised) in which young, mostly male participants compete for increasingly lucrative prize money and sponsorship contracts. Taking up Jo Bryce and Jason Rutter's (2005) challenge to confront the ways girl gamers are rendered ‘invisible’ by gamers, researchers and designers, this paper maps the various ways women participate in a set of practices around the organisation, promotion and performance of competitive gaming, framed as the exclusive domain of (young, straight, middle class) male bodies. Mothers flying their sons' teams to events all over North America, female players participating in tournaments or promotional models operating sponsorship booths, the women who participate in competitive gaming tournaments negotiate different expectations and carry out different kinds of embodied work. Each of these ‘roles’, however, is tenuously maintained within a community that most commonly reads female participation in sexualised terms: mothers at events describe themselves as ‘cheerleaders’, female players risk being labelled as ‘halo hoes’ and promotional models become ‘booth babes’.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".