Prospective Passion and Social Capital within DotA 2 Players
Bibliographic record
Abstract
The Dualistic Model of Passion (obsessive and harmonious passion) can explain motivations for videogame play along with associated outcomes, such as the development of social capital; however, existing research exploring passion and social capital in videogaming has been cross-sectional. In the current study we surveyed players of DotA 2 at three time points, over six months (T1 n=462, T2 n=182, T3 n=115), to explore the stability of passion for DotA 2 over time and how such passion may lead to the development or erosion of social capital. Our key findings include that passion for playing DotA 2 is relatively stable over time and that harmonious passion predicts future bridging social capital, while obsessive passion predicts future bonding social capital. Importantly, our findings suggest the absence of a "slippery slope" scenario in which players who have a healthy pattern of engagement development obsessive passion or problematic play. Equally, however, our findings also suggest that those who are obsessive are unlikely to naturally trend towards a more harmonious style of engagement over time. We consider the implications of our findings for health practitioners, players and videogame developers and identify the differences between our longitudinal findings and the existing cross-sectional research.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".