The critical effect : evaluating the effects and use of video game reviews
Bibliographic record
Abstract
Game reviews play an important role in both the culture and business of games -the words of a reviewer can have an influential effect on the commercial success of a video game.While reviews are currently used by game developers to aid in important decisions such as project financing and employee bonuses, the effect of game reviews on players is not known.Additionally, the use of game reviews to improve evaluation techniques has received little attention.In this thesis we investigate the effect of game reviews on player experience and perceptions of quality.We show that negative reviews cause a significant effect on how players perceive their in-game experience, and that this effect is a post-play cognitive rationalization of the play experience with the previously-read review text.To address this effect we designed and deployed a new heuristic evaluation technique that specifically uses game reviews to create a fine-grained prioritized list of usability problems based on the frequency, impact, and persistence of each problem.By using our technique we are able to address the most common usability problems identified by game reviews, thus reducing the overall level of negativity found within the review text.Our approach helps to control and eliminate the snowballing effect that can be produced by players reading reviews and subsequently posting their own reviews, and thus improve the commercial success of a game.
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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 | 0.063 | 0.423 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".