The Impact of Playing the Otome Game on Single Women’s Interest in Real-life Romantic Relationships
Bibliographic record
Abstract
Otome games have gained popularity among women, offering a space to fulfill their emotional and romantic desires. This study explores how playing Otome games may reduce single womens interest in real-life romantic relationships through the lens of evolutionary psychology. We designed a study with 200 single Chinese women aged 18-35 who have not previously played Otome games, to play an Otome game called Love and Deepspace for three months and we will record their gaming time and monetary expenditure. The study aims to test the hypothesis that increased engagement in Otome games, measured by time and money spent, negatively correlates with participants interests in real-life romantic relationships. Our research examines how supernormal stimuliidealized traits in virtual romantic partnersappeal to female mating preferences, contributing to an evolutionary mismatch. This mismatch may have significant implications for how modern virtual dating experiences shape romantic behaviors, with potential effects on societal trends such as declining interest in real-life romantic relationships.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".