Apple and the American Revolution: Remembering Why We Have the Fourth Amendment
Bibliographic record
Abstract
This essay provides a concise history of events prior to the Revolutionary War that led to the adoption of the Fourth Amendment and relates that history to current controversies over the use of search warrants to obtain electronically stored information from cell phones and email accounts in the cloud, in particularly the FBI's attempt to get court orders forcing Apple to create and give to the government software eliminating the user privacy and security features of the iPhone and the pending Microsoft lawsuit against the Department of Justice challenging email search gag orders.Compelling comparisons are drawn between the infamous Lord Halifax general warrants used in 1763 to seize all of a suspect's private papers and search them for evidence of seditious libel and warrants currently used by the federal government to seize the entire contents of a cell phone or of a cloud account and then conduct review of every item of digital data.The essay concludes with legislative proposals to extend existing statutory safeguards for wiretapping and electronic surveillance to search warrants for electronically stored information, and provide notice and a right to a hearing before such warrants are executed.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".