Play with Purpose: Exploring digital games to encourage gender equity in STEM
Bibliographic record
Abstract
Playing digital games is one of the most popular activities amongst youth today. Research studies have connected young women who participate in digital games as three times more likely to pursue a career in Science, Technology, Engineering and Math (STEM) fields. Although STEM skills are considered essential for the future of an innovative society, there exists a gap of the women represented in these fields. Exploring digital games provides a potential solution to closing this gap, however the digital games environment is not as inclusive for women as it could be. \n \nAs a result, this research focuses on utilizing digital games to support STEM education for young women in Canada. The research begins with a literature review, anonymous survey, semi-structured interviews, and a co-creative workshop to understand the experiences of women in digital games and STEM in Canada today, and to structure these insights through a Deconstruction:Reconstruction process. Systems mapping and foresight tools were also used to generate a desired future and guide strategic recommendations. \n \nInsights suggest a desire for education on digital etiquette and the development of a digital space where young women can practice building confidence while having their voices heard. Three opportunity spaces were identified as key leverage points to intervene within the digital game system, including opportunities between Educational, Recreational and Gaming Industry subsystems. These opportunity spaces were further established as two recommendations that provide strategic initiatives for educational institutions to engage with digital games, involving different stakeholders in the system. By changing the experiences women currently have while engaging in digital games, perhaps more women would be inclined to participate in play and therefore engage in STEM fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".