What are the influences on gameplay and the impacts of a player's choice of protagonist gender?
Bibliographic record
Abstract
This thesis aimed to explore factors, which players consider when choosing their protagonist's gender and what impact that choice has on gameplay. We used two popular games as reference: “Assassin’s Creed Odyssey“ (Ubisoft Quebec, 2018) and “Baldur's Gate 3” (Larian Studios, 2023). This study was conducted by interviewing participants and sending out surveys among the two games’ communities with a total of 493 participants sharing their answers. The sample group mainly consisted of players from several European countries and the United States of America, with 280 identifying as male, 181 as female, and 32 as non-binary. It was found that the majority of players agreed that visual character customization and gender choice did provide them a greater sense of immersion. Furthermore, the storyline seemed to be a framing factor influencing player choices by offering a setting players carefully considered when designing/choosing their protagonists. These results can be of use to game designers who wish to gain an understanding of players’ gender choices and impacts on their role-playing-game gameplay experience. Additionally, further research is suggested to get a deeper understanding of outlying factors that players might consider when choosing a character.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".