Bibliographic record
Abstract
Digital games have become a social medium. Players are often socially motivated to play games and actively seek out games that offer social interactions. Early studies on games such as World of Warcraft demonstrate that players can form meaningful bonds within the game. Catering to this trend, most game titles now include multiplayer experiences in their gameplay. Despite the growing popularity of social elements within play, we still have little empirically-founded guidance on how to effectively design for social experiences. If we want to design for social play, we have to understand what makes games social. What are the properties of play that are responsible for facilitating social ties between players? We address this question by synthesizing the exiting literature on design recommendations for social play into identify overarching properties of play that we think are the most prolific in literature: cooperation and interdependence. We perform two experimental studies demonstrating how games facilitate trust between players and how cooperation and interdependence are crucial properties of social play. Furthermore, we validate our framework in a field study, investigating the experiences within games that predict in-game social capital. We demonstrate that interdependence and toxicity are strongly linked to the social capital our participants experience in their gaming communities. We also illustrate how in-game social capital is negatively associated with feelings of loneliness and positively associated with need satisfaction of relatedness outside of the context of play. Overall, our findings emphasize how strongly the experiences within the game affect the social ties that emerge from play, suggesting that informed design choices are crucial for the success of social games. This dissertation also contributes to the ongoing debate about the effects that in-game relationships have on the player’s mental health—we show a strong positive link between in-game social capital and markers for psychological well-being. It is easy to disregard in-game relationships, as they are fundamentally distinct from the in-person ones we think of as natural. Yet we cannot ignore the emergence of digital games as a social medium. The more we understand the underlying elements of social play, the better we can design games that bring people closer together.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".