Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This Article presents a framework for analyzing cell phone searches by employers. The framework proposed in this Article is structured around two primary variables: (1) whether the employee whose cell phone is searched works for a public or private employer, and (2) whether the cell phone is owned by the employer or employee. The starting point for developing a framework for cell phone searches is the Fourth Amendment to the United States Constitution, which prohibits “unreasonable searches and seizures” by state actors, including public employers. To be reasonable, a Fourth Amendment search or seizure must ordinarily be justified by a warrant or warrant exception. One warrant exception of particular relevance here is the “workplace exception” established by the United States Supreme Court in O’Connor v. Ortega, which allows for certain employer-initiated searches on the basis of an employer’s own determination of reasonable suspicion. Other key Supreme Court precedents that impact employee cell phone searches include City of Ontario v. Quon, which applied the O’Connor exception to uphold an employer’s review of text messages on an employer-owned device; and Riley v. California, which established heightened privacy protections for personally owned cell phones.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it