Bibliographic record
Abstract
According to the third-party doctrine, a person has no reasonable expectation of privacy in information that has been shared with others—including a bank, phone company, or credit card company. The doctrine got its start through an appeal to a locatable observer who corresponds, in literary terms, to a narrator with a limited perspective. This is the kind of perspective that courts have traditionally emphasized when explaining how to assess probable cause. The third-party doctrine turns the limited perspective into an omniscient one. The doctrine takes apparently private conduct and classifies it as public, effectively treating the perspective of the “arresting officer” as if it could encompass large quantities of information, widely distributed in space and time. The discussion here examines a recent defense of the third-party doctrine that similarly collapses the limited and omniscient viewpoints. Then, after exploring the narrative analogy by reference to literary analyses of the omniscient narrator in Victorian fiction, the discussion ends by considering the analogy in relation to contemporary modes of omniscient narration.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".