Comprehension of Ads-supported and Paid Android Applications: Are They\n Different?
Bibliographic record
Abstract
The Android market is a place where developers offer paid and-or free apps to\nusers. Free apps are interesting to users because they can try them immediately\nwithout incurring a monetary cost. However, free apps often have limited\nfeatures and-or contain ads when compared to their paid counterparts. Thus,\nusers may eventually need to pay to get additional features and-or remove ads.\nWhile paid apps have clear market values, their ads-supported versions are not\nentirely free because ads have an impact on performance.\n In this paper, first, we perform an exploratory study about ads-supported and\npaid apps to understand their differences in terms of implementation and\ndevelopment process. We analyze 40 Android apps and we observe that (i)\nads-supported apps are preferred by users although paid apps have a better\nrating, (ii) developers do not usually offer a paid app without a corresponding\nfree version, (iii) ads-supported apps usually have more releases and are\nreleased more often than their corresponding paid versions, (iv) there is no a\nclear strategy about the way developers set prices of paid apps, (v) paid apps\ndo not usually include more functionalities than their corresponding\nads-supported versions, (vi) developers do not always remove ad networks in\npaid versions of their ads-supported apps, and (vii) paid apps require less\npermissions than ads-supported apps. Second, we carry out an experimental study\nto compare the performance of ads-supported and paid apps and we propose four\nequations to estimate the cost of ads-supported apps. We obtain that (i)\nads-supported apps use more resources than their corresponding paid versions\nwith statistically significant differences and (ii) paid apps could be\nconsidered a most cost-effective choice for users because their cost can be\namortized in a short period of time, depending on their usage.\n
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".