NOT SUFFERING FOOLS GLADLY: CRAFTING PROSOCIAL COMMUNITY IN ONLINE MULTIPLAYER MINECRAFT
Bibliographic record
Abstract
Over 700 million people worldwide are socializing and spending time, sometimes significant amounts, in online multiplayer games, and these social spaces can be important sites of community. Unfortunately, levels of civility, aggression, and mutual helping can vary significantly between game spaces. Given their ubiquity and importance in so many people’s lives, it is critical to understand how a prosocial community can be created and maintained over time in these spaces for those who want them. This research uses virtual ethnography and interpretive phenomenological analysis to examine how moderation and community development strategies, game design elements, and player behaviours are experienced and can be influenced by players in prosocially-oriented online multiplayer Minecraft servers. It is clear that it is the prosocial orientation of players and the commitment, social skill, and integrity of server moderators that is most key to creating and maintaining a prosocial gaming environment and that although game design can support prosociality, game design factors appear to be much less important overall. Attracting the right players—and refusing entry to the wrong ones—is the most important concern.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".