On the Determinants and Outcomes of Passion for Playing Pokémon Go
Bibliographic record
Abstract
In 2016, Pokémon Go became the most popular smartphone game. Despite the increasing popularity of this augmented reality game, to date, no studies have investigated passion for playing Pokémon Go. On the theoretical basis of the Dualistic Model of Passion, our goal was to investigate the associations between Pokémon Go playing motives, passion, and impulsivity. A total of 621 Pokémon Go players participated in the study (54.9% female; Mage = 22.6 years, SDage= 4.4). It was found that impulsivity was more strongly associated with obsessive passion (OP) than with harmonious passion (HP). HP was associated with adaptive motives (i.e., outdoor activity, social, recreation, and nostalgia), while OP was associated with less adaptive motives (i.e., fantasy, escape, boredom, competition, and coping). Therefore, in line with the Dualistic Model of Passion, HP and OP for playing Pokémon Go can predict an almost perfectly distinguished set of adaptive or maladaptive playing motives, and OP has a noteworthy relationship with impulsivity as a determinant.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".