Bibliographic record
Abstract
Perhaps “the” question in this age of workplace technological innovation concerns the amount of privacy employees should have in electronic locations in the workplace. An important related question is whether public-sector and private-sector employees, who have different legal statuses under the state action doctrine, should enjoy the same level of workplace privacy. Recently, in the Fourth Amendment workplace privacy case of City of Ontario v. Quon, the United States Supreme Court considered both of these questions. Quon involved alleged privacy violations by a city police department when it audited an officer’s text messages from his city-issued pager. In a cryptic decision, Justice Kennedy held for a unanimous Court that assuming the officer had a reasonable expectation of privacy in the pager, the City’s search of the pager was reasonable under two possible legal tests. First, under the plurality test enunciated by the Supreme Court in O’Connor v. Ortega, it was reasonable because it was motivated by a legitimate work-related purpose and was not excessive in scope. Second, under the test outlined by Justice Scalia in his concurring opinion in O’Connor, it was reasonable because it would be considered “reasonable and normal” in the private-sector workplace. To varying degrees, both of these legal tests suggest that questions of workplace privacy in the public and private sectors should be treated the same. Rather than elevating private-sector privacy rights to the public-sector level, however, Quon suggests that public employee workplace privacy rights should be reduced to the level of employees in the private sector. Maintaining that public-sector workers are entitled to greater levels of privacy protections based on the text of the Constitution, the power of the government as employer, and the critical oversight role public employees play in American democracy, this Article argues for a new, two-step workplace privacy analysis which first focuses on the purpose of the search and then applies presumptively the Fourth Amendment’s warrant and probable cause requirements to those searches undertaken for investigatory purposes.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".