Gamers and Video Games Users: What’s the Difference?
Bibliographic record
Abstract
The term “gamer” is commonly used to refer to individuals who play video games frequently. However, building on Self- Determination theory (SDT) and the Dualistic Model of Passion (DMP), we argue that it may be more theoretically and practically useful to operationalize individuals as “gamers” versus “non- gamers” based on their identification and passion for gaming rather than based on how frequently individuals play video games. Thus, the purpose of the present study is to compare four groups, those who identify as gamers or non-gamers with those who have frequent use or not, on independent variables of gaming engagement, motivation, and problematic gaming. Participants (N = 1,050; 70.1% males; Mage = 23.74 years, SD = 6.48 years) completed measures online. Results revealed that identifying as a gamer was a stronger predictor of levels of gaming engagement, motivation, and problematic gaming compared to frequent use. Findings highlight the potential of SDT and DMP for understanding gamer characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".