An Examination of the Current Rating System used in Mobile App Stores
Why this work is in the frame
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Bibliographic record
Abstract
Unlike products on Amazon.com, mobile apps are continuously evolving, with new versions rapidly replacing the old ones. Nevertheless, many app stores still use an Amazon-style rating system, which aggregates every rating ever assigned to an app into one store rating. To examine whether the store rating captures the changing user satisfaction levels regarding new app versions, researchers mined the store ratings of more than 10,000 mobile apps in Google Play, every day for a year. Even though many apps' version ratings rose or fell, their store rating was resilient to fluctuations once they had gathered a substantial number of raters. The conclusion is that current store ratings aren't dynamic enough to capture changing user satisfaction levels. This resilience is a major problem that can discourage developers from improving app quality.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it