On The Costs and Benefits of Gaming: The Role of Passion
Bibliographic record
Abstract
The dualistic model of passion defines passion as a strong inclination toward a self-defining activity that a person likes and values and in which he or she invests time and energy. The model proposes two distinct types of passion: harmonious and obsessive passion that predict adaptive and less adaptive outcomes respectively. In the present research, we were interested in assessing both the negative and positive consequences that can result from gaming. Participants (n = 222) were all players involved in massively multiplayer online games. They completed an online survey. Results from a canonical correlation revealed that both harmonious and obsessive passion were positively associated with the experience of positive affect while playing. However, only obsessive passion was also positively related to the experience of negative affect while playing. In addition, only obsessive passion was positively related to problematic behaviors generally associated with excessive gaming, the amount of time spent playing, and negative physical symptoms. Moreover, obsessive passion was negatively related to self-realization and unrelated to life satisfaction. Conversely, harmonious passion was positively associated with both types of psychological well-being. This general pattern of results suggests that obsessive passion for gaming is an important predictor of the negative outcomes of gaming, while harmonious passion seems to account for positive consequences. Future research directions are discussed in light of the dualistic model of passion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".