Bibliographic record
Abstract
To better understand boys’ privilege and girls’ educational disadvantage with regard to video games, this presentation takes up Jo Bryce and Jason Rutter’s recent challenge to confront the ways girl gamers are rendered “invisible” by gaming communities, researchers, and designers. From the fall of 2004 to the spring of 2005, Jennifer Jenson and Suzanne de Castell’s EGG (Education, Gender and Gaming) project carried out a gaming club for girls at a local elementary school in the Greater Toronto Area. Not only did the project provide female students with a “safe” space to attain and practice gaming competency – which they were consistently denied at home – but it provided an audio-visual record of girls’ play allowing for critical explorations of gendered gaming practices. At one point in the footage, a gaming session between five girls is interrupted when two boys enter the scene and try to hijack their play. Using the MAP (Multimodal Application Program, developed by Suzanne de Castell and Jennifer Jenson) tool to visually chart and analyze the co-ordinated reactions of the girls as they put down their controllers and hold their bodies immobile during the boys’ disruption, this paper explores the tenuous relationship to video games these girls enjoy, even within a space ostensibly devoted to their play.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".