You teach me and I’ll teach you: The role of social interactions on positivity elicited from playing Pokémon GO
Bibliographic record
Abstract
Augmented Reality video games such as Pokémon GO have a structure that encourages face-to-face social interactions between players, leading to potentially unique benefits for positivity (positive affect). This study investigated how participants’ social interactions while playing Pokémon GO relate to their positivity after gameplay, crucially, after accounting for other non-social factors typically associated with positivity (participants’ satisfaction with their game accomplishments). Participants were 108 Pokémon GO players, consisting of 54 dyads who signed up for the study together. Dyads were asked to play Pokémon GO together for eight sessions over 2 weeks, and to report on their gameplay experiences and positivity after each session. Multilevel modelling analyses revealed that more positive social interactions with their gameplay partner incrementally predicted participants’ greater positivity post-gameplay. The association between positive social interactions and greater positivity was accentuated for participants who reported more frequent noxious mood states (depressive symptoms) at the start of the study. Findings suggest that above and beyond typical contributions such as achieving game accomplishments, there may be affective benefits for Pokémon GO players from the social interactions they have within the game, especially for those with noxious mood states.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".