Women expert gamers: portrait of an understudied population
Bibliographic record
Abstract
Introduction: Very little is known about women who persist in competitive gaming environments and their unique characteristics. Thus, the current study aims to provide an initial portrait of these women. Methods: A subsample of 128 female gamers, selected from a larger internet survey, completed an online questionnaire assessing sociodemographic factors, gaming experience, motives to play, and mental health variables. Self-reported expert gamers were compared to casuals on these variables. Then, a backward logistic regression allowed to identify the best predictors of being an expert gamer. Results: Experts reported higher gaming involvement and higher levels of specific gaming motives. Experts also tended to engage in specific game types and to report more positive outcomes than casuals. Finally, gaming involvement, competition motive, self-esteem, and game genre were the best predictors of group membership. Discussion: This study highlighted several unique characteristics of female expert gamers, suggesting an important role of personal and environmental strengths, gaming involvement, and gender representation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".