On Social Contagion in Gamification
Bibliographic record
Abstract
Social connections shape our behaviour because of peer pressure and social contagion. This phenomenon is amplified in online networks by particularly influential individuals: influencers. Although this concept originated in social media, recent research shows how influencers can also exist in games and affect players' long-term retention. Prolonged retention caused by influencers could benefit gameful systems, especially if the system's goal is positive behavioural change. Retention is desirable because it can aid in internalizing new habits. Therefore, we investigated retention influencers' presence within a location-based persuasive gamified system (Play&Go) and their influence on other behaviours (i.e., the pursuit of a gamification goal), via social network analysis techniques. Results show how retention influencers exist in Play&Go and how studying different influence types (which push the systems' goals) may lead to different influencer groups. Our findings emphasize the importance of social mechanics in location-based gamification and discuss the value of understanding a player community to improve game design.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".