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Record W590646590

Did My Boss Just Read That? Applying a Coding vs. Content Distinction in Determining Government Employees’ Reasonable Expectation of Privacy in Employer- Provided Electronic Communication Devices After City of Ontario v. Quon, 130 S. Ct. 2619 (2010)

2011· article· en· W590646590 on OpenAlexaboutno aff
Lori Hoetger

Bibliographic record

VenueLincoln (University of Nebraska) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsBossCoding (social sciences)Government (linguistics)Internet privacyContent (measure theory)BusinessPsychologyComputer scienceMathematicsStatisticsLinguisticsEngineering
DOInot available

Abstract

fetched live from OpenAlex

With the rapidly increasing prevalence of cell phones, e-mail, and other forms of electronic communication, courts are forced to answer the question of whether individuals have a reasonable expectation of privacy in electronic communication devices. Courts increasingly face situations where public employers search their employees’ work-provided communication devices, and the employees claim a violation of an expectation of privacy. Historically, the Court has distinguished information available to third parties from information intended only for the recipient’s eyes, which the sender attempted to keep secret from others. In 1877, the Supreme Court applied the Fourth Amendment to sealed letters sent via the United States Postal Service. In Ex parte Jackson, the Supreme Court determined an individual who mails a letter has a reasonable expectation of privacy in the content of the sealed letter; on the other hand, the individual has no reasonable expectation of privacy in the addressing information on the outside of the envelope. In 1979, the Court applied a similar distinction in determining an individual does have a reasonable expectation of privacy in the content of telephone calls, but not the number he or she has dialed. With technology advancing, the question has arisen of whether a similar distinction should apply to determine the reasonableness of a privacy interest attached to electronic communications. The United States Supreme Court faced this question in City of Ontario v. Quon, in which the Court held the Fourth Amendment does not protect an employee’s text messages from a public employer’s search. Quon, a police officer in the City of Ontario, claimed his supervisors violated his reasonable expectation of privacy when they searched the content of his text messages sent on his employer-provided text messaging pager. The Court declined to determine whether Quon, and by extension other public employees, would have a reasonable expectation of privacy in such devices. Instead, the Court determined that, regardless of Quon’s expectation of privacy, the City of Ontario was reasonable in searching the pager. This Note begins by exploring the relevant history of the Fourth Amendment search and seizure provision as applied to communications and government employers. Part III discusses the Court’s opportunity in Quon to apply a set standard to text messages, and argues the Court should make more definitive statements determining government employees’ privacy interests in the future. Part IV gives recommendations for lower courts in handling the nebulous area left by the decision in Quon. The Court should follow the standard first espoused in Ex parte Jackson: individuals have a reasonable expectation of privacy in the content of their text messages, but not the addressing information.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.790

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.010
Scholarly communication0.0080.006
Open science0.0020.002
Research integrity0.0090.006
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.078
GPT teacher head0.220
Teacher spread0.142 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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