Bibliographic record
Abstract
It is unquestioned that many American citizens place great value on maintaining privacy from the spying eyes of government. After the 2013 leak of classified NSA information by CIA employee Edward Snowden, there has been a continuous conversation regarding the protection of technological privacy—the personal information that we store on our desktops, laptops, tablets, and phones. Of these technologies, cell phones are possibly the most central to our everyday lives. We carry our phones nearly everywhere with us, usually clenched tightly in our palms. They contain our personal and work emails, text messages with loved ones, catalogs of pictures documenting the recent months and years of our lives, our banking information, and secrets that may be too private to keep where others may stumble upon them. The American government and state officials are utilizing a newly developed device that directly affects this cellular privacy. These devices are called cell site simulators. With cell site simulators, officials are able to mimic cell towers and collect cellular data from any and all phones within a given geographic area. This information enables officials to pinpoint where a certain cell phone is located, and they are then able to use that information in a variety of different ways. The devices can be effective in narcotics investigations, tracking avalanche and kidnapping victims, as well as in other non-criminal investigations. The most obvious benefit of cell site simulators is large-scale crime reduction, but at what cost? The device is relatively new, so few states have developed legislation concerning its use, and Congress has not codified any guiding acts. There have been several Supreme Court decisions concerning personal privacy and its relation to physical searches of cell phones and the data contained therein, as well as the use of technological surveillance in constitutionally protected areas. However, there is yet to be a decision concerning the constitutionality of searches using cell site simulators. It is imperative that Congress draft a clear, in-depth bill enumerating when, how, and by whom a cell site simulator may be used. When drafting the bill, several considerations must be taken into account, including Supreme Court jurisprudence concerning Fourth Amendment protections against unreasonable searches and seizures and the current status of legislation in each state of the United States. These considerations are addressed and discussed in turn.
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 | 0.032 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.039 | 0.037 |
| Insufficient payload (model declined to judge) | 0.076 | 0.024 |
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".