Assessment of the Impact of a Privacy Policy Change on User Behavior and Marketing effects in Online Applications
Bibliographic record
Abstract
This research aims to assess the impact of privacy policy changes on user behavior within online applications.Specifically, it focuses on changes in online application usage behavior in relation to privacy disclosures and the influence of privacy policy changes on website application.The research focuses on the states of California and Virginia, which were among the first to implement privacy laws.It evaluates the impact of these specific privacy laws in terms of their enactment timing and level of privacy protection.The study analyses online marketplace data and user data from September 2021 to December 2023, segregating them by state.The projected results suggest that online applications collecting and sharing more personal data, such as home devices, may be more greatly impacted by privacy policy changes.It is expected that some mobile apps may offer lower pricing in exchange for increased collection and sharing of personal data.This research contributes to the existing literature by examining the impact of privacy policy changes in the United States market.It aims to understand the overall impact on user behavior and the market, considering variations in privacy disclosure requirements.By analysing the effects of privacy policy changes, this study provides valuable insights into user behavior and marketing effects within online applications.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".