The influence of video game mechanics on youth’s development of an esports team : an actor network theory analysis
Bibliographic record
Abstract
Video games, both casual games and esports titles alike, often take the blame when youth exhibitpoor academic performance in school, aggressive behaviors, or anti social tendencies, a tendencythat risks the relegation of potential positives gaming can bring. Furthermore, within theacademic literature centered on the impact of video gameplay on youth, there also exists atendency to conceptualize the relationship between video games and players as a singular, ‘oneto one’ relationship devoid of any larger, physical community that is not online, as well as anotable lack of discussion on how specific game mechanics influence gamer communities. Thisthesis aims to address both of these social and academic issues through an eighteen month longstudy, employing participant observation methods, of how game mechanics, or non humandigital actors more broadly, influenced the formation of a youth esports team within the contextof a local youth center in Montréal, Canada. Building off of the work of Bruno Latour and JohnLaw, this study attempts to shed light on the relatively unexamined social processes thatpermeate physical gaming spaces through the lens of Actor Network Theory (ANT) while alsopositing a contribution for new ways to use ANT as well. The major findings that emerged fromthe study reveal the strong influence that non human digital actors had in the processes ofyouth’s identity formation and communication practices, all processes that facilitate strong socialconnections and bonds that allowed the youth, and the larger community around them, to growand flourish as both community members and gamers. In displaying some of the positive aspectsthat emerged from the formation of a youth sports team, this research hopes to dispel some of thebigotry and stigma associated with video games and enable educators / researchers to betterunderstand the often invisible ways that non human digital actors operate within games andgaming communities when seeking to nurture or study said communities in the future
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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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".