The Argonauts of esports Practice: Zooming in on the practice-networks of everyday gamers
Bibliographic record
Abstract
Should people be concerned about everyday gamers’ participation in esports practices? This dissertation will address the question by exploring how everyday gamers’ practices are informed by professional gaming (esports). The rise of professional competitive video gaming has exploded in the past two decades. Billions of dollars of prize money have been awarded to thousands of people around the globe since the turn of the century. While a valorized few players have been able to professionalize their gaming, millions of everyday gamers spend countless hours participating in these same practices with no hope of ever being professionals. Who benefits from the perpetuation of the instrumental in-game practices? Who shapes the practices of groups attempting to organize their esports gaming? How do the affordances of nonhuman actors shape the practices of everyday gamers participating in esports gaming? Can the creation of esports media really empower everyday gamers? Using a micro-ethnographic approach this project will trace practice-networks of a student gaming club at the University of Calgary. The approach draws from the theoretical perspectives of practice theory and actor-network theory. Assessing whether the practices elucidated through this framework should be of concern will be accomplished primarily through the concept of participatory culture. Many scholars have weighed in on the value of participatory culture in contemporary society and those positions will be used in the evaluation of the practice-networks of the gaming group. Through this perspective the study will zoom in on the in-game practices of everyday gamers. Through a series of interviews, event observations and time spent in the game I will examine what resources esports practices require and what benefits players receive for their execution. Through engaging with the concept of gamer capital, and expanding it, I will trace the different pressures placed on everyday gamers in relation to in-game practices. I will then zoom out to examine two of the actants, the Students’ Union and Riot Games, which shaped the organization of the student club. The esports game explored in this study forces everyday gamers to seek out others to form teams to play the game. In their efforts to organize their gaming on campus two main actants exerted influence over the organization of the group. I will trace how this influence was exercise. The group drew on various nonhuman actors, from social media to university lecture halls, in their participation in esports practice. I will then zoom in on how the affordances of these actors shaped the practice of the club. Finally, I will explore a competitive series produced by the group and streamed on YouTube, to assess whether this kind of participation is empowering everyday gamers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".