Studying The Consistency Of Star Ratings And Reviews Of Popular Free Hybrid Android And Ios Apps
Bibliographic record
Abstract
We study 68 hybrid app-pairs, i.e., apps that exist both in the Google Play<br> store and Apple App store. We find that 33 out of 68 hybrid apps do not receive<br> consistent star ratings across platforms. We run Twitter-LDA on user reviews and<br> find that the star ratings of the reviews that discuss the same topic could be up<br> to four times as high across platforms. Our findings suggest that while hybrid<br> apps are better at providing consistent star ratings and user reviews, they do not<br> improve the consistency in star ratings and user reviews at a great extent when<br> compared to cross-platform apps that are built natively. Hence, developers should<br> not solely rely on hybrid development tools to achieve the consistency in the star<br> ratings and reviews given by their users.<br>
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".