“Try to Zone Out and Not Think of the World That Was Exploding”: The Experience of Free-to-Play Games and Everyday Life During the Pandemic
Bibliographic record
Abstract
As the COVID-19 pandemic struck, lives of millions of people were suddenly transformed. Given the way free-to-play (F2P) games are integrated into everyday life, the pandemic presents unique opportunity to explore how players' relationships with F2P games have been impacted. This study aims to understand how the first year of the pandemic impacted F2P gaming practices. Semi-structured interviews were conducted with 46 Canadian F2P players. The analysis revealed three categories of change to participants' relationship with F2P practices: (1) disruptions to players' everyday lives, (2) the space and time in which play takes place, and (3) the role of gaming in everyday pandemic life. This study highlights changes within the players' living environment that enable these games to become an integral part of everyday life. Since some changes have lasted over time, it's important to explore whether their impacts on players' relationship with F2P games have also persisted over time.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".