Bibliographic record
Abstract
Technology’s capabilities are rapidly expanding and apps, officially known as applications, exemplify this growth. Downloading an app on a smartphone or a tablet expands its capabilities, supplying the device with the power of a computer yet far more mobile. However, apps’ capabilities also have a more sinister side, collecting mass amounts of personal information from users without their full knowledge. Given this threat to consumer privacy, new legislation must be developed and updated to safeguard users against privacy infringements and maintain trust in the marketplace. This paper demonstrates the gap in Canadian privacy regulation regarding apps and presents that additional legislation is required for greater accountability, transparency, and user choice. Smartphones are increasingly populating the mobile landscape, with a sizable amount of personal information flowing through these powerful devices. One concern is that smartphones are highly mobile and always-on devices that are perpetually with the user, allowing location tracking. A small screen size also impedes companies’ ability to effectively communicate to the user what personal information is being collected, and the rapid development lifecycle of apps increases the probability of inadequate considerations. Although consumers value information obtained through using apps, they also wish to protect their personal information and privacy. Likewise, businesses value consumer behaviour information acquired through consumer usage of apps, information that enables them to understand and effectively target their consumers. Consequently, consumers’ personal information holds value— and challenges—for consumers and producers of apps alike. In 2007, the first app was created for Apple’s iPhone. Since then the app market has exploded, as users are increasingly using apps to access location information, social media, entertainment, and other information. As the app industry is largely unregulated, this presents privacy concerns as to the amount of personal information being collected, with whom it is shared, and for how long it is retained. In essence, a lack of disclosure and transparency exists on the part of app developers and providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".