There's an Amendment for That: A Comprehensive Application of Fourth Amendment Jurisprudence to Smart Phones
Bibliographic record
Abstract
The near ubiquity of smart phones in American society raises a multitude of issues as courts attempt to fit the use of this new technology into old property analogies. This Note specifically addresses the application of the Fourth Amendment's proscription against unwarranted search and seizures to these devices. It traces Fourth Amendment jurisprudence through Katz v. United States and more recent cases such as City of Ontario v. Quon and notes a general emphasis on property analogies. However, this emphasis is deemed insufficient in its application to smart phones, given that they do not neatly fall into any prior categories. The Note poses a refinement of the model for dealing with the data encountered in and around smart phones, suggesting that it be divided into two dichotomies of local versus remote data and coding versus content data. In doing so, it suggests some methods for law enforcement to meet their needs while respecting the smart phone owners' reasonable expectations of privacy in their data.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.027 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".