PLEASURE+GENDER+PLAY
Bibliographic record
Abstract
From the start, play and game studies scholars have investigated the experiences of women and girls who play games online, as well as gendered assumptions around digital as well as non-digital play (Brunner et al., 2000; Bryce & Rutter, 2002; Delamere & Shaw, 2008; Fron et al., 2007a, 2007b; Pearce, 2009). Scholars have challenged ideas such as that girls and women have weaker gameplay skills than boys and men (Jenson & De Castell, 2008), that women are not interested in competitive play (Taylor, 2006), that girls and women are different in their play experiences and interests (Royse et al., 2007) and that women are not frequent or loyal players (Consalvo & Begy, 2015; Williams et al., 2009). However, there is still more to learn about how women, girls, boys, men, nonbinary and other individuals play, as well as how gender can play an important role beyond as an identity marker in playful expressions as well as normative expectations for play. This panel offers new ways of examining how gender, games, and other forms of online play, can be analyzed and understood. These four papers argue for a more nuanced understanding of gender and play, further challenging gaming culture’s preoccupation that certain games and certain styles of play are more “valid” than others (Consalvo & Paul, 2019). To do that, we offer fresh analytical tools, different theoretical lenses and underexplored sites for study. $2 “No Need For Speed” makes a unique contribution to gaming and play literature, offering a new articulation of the temporal experiences within and external to game play - especially in COVID/pandemic times. In particular, the authors argue that the concept of “slow gaming,” might offer new possibilities for both our experiences of play and the way that time within the games industry itself is being reconceptualized. The authors offer three different games as examples of how “slow gaming” challenges our relationship to play, domesticity, notions of gender, and labor practices within the gaming industry more broadly. This paper argues that playing slow games, or playing games slowly, might provide a unique political rejoinder to contemporary life under late capitalism. $2 Two papers in this panel bring underutilized theoretical frameworks to the study of gender and games: examining how socioeconomic class and boundary keeping intersect with gender and gameplay in important ways. The presentation “Working for hearts: Social class and time management games” reads popular casual games such as Sally’s Spa through an intersectional critique. Adding to gendered examinations of casual games (Chess, 2012, 2017), this paper brings in a critique of social class. It does so through exploring the classed positions of jobs in these games, as well as how the player’s agency is limited both through classed expectations of certain occupations as well as further undermined by particular design decisions and gameplay mechanics as well as game narratives. It demonstrates how class is an important aspect of identity that can help us better understand gaming representations. The second paper to bring in underutilized theory is “Gendered expectations of playing nice, boundary keeping and problematic/toxic behaviors in casual video game communities.” This paper offers a different way of understanding the role of toxic behavior and players in game communities: through the sociological lens of boundary keeping. While not dismissing the real effects of harassment, it explores how activities such as trolling and other problematic gameplay is defined differently within different player groups, how it can strengthen some in-game communities or spur the creation of groups dedicated to combating such problems, and in the process helping to further enrich and make more inclusive gaming culture. $2 “Girls, Platforms, and Play” examines an offline form of gendered play and competition – pre-teen and teen girls riding hobbyhorses – and how the activity has been differently contested and/or constructed on two platforms: YouTube and Instagram. Legacy media video content of hobbyhorse competitions uploaded to YouTube inevitably have led – given YouTube’s largely antisocial comment culture (Burgess & Green, 2018) – to hobbyhorsers’ activities to be delegitimized for a number of reasons by commenters: mainly, because it’s just girls playing with toys, not participating in a sport; or because it is an athletic endeavor, but its participants should compete in a “real” sport, like track and field; or because it’s not real equestrianism. Instagram's affordances, which help encourage connections among subculture participants and the creation of communities (Leaver, Highfield, & Abidin, 2020), have allowed hobbyhorse enthusiasts to create a space of their own online.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".