Kwiatkowski : Privacy Protection and Risk Analysis: Losing the Forest in the Telephoto Shots of the Trees
Bibliographic record
Abstract
Checklists of factors are a helpful feature in assisting courts to determine how to find the proper balance in a variety of situations. Properly used they can help to achieve a certain level of uniformity and predictability, though they do not guarantee it. However, it is also important to recognize that checklists are a way of getting at the right analysis — they are not in and of themselves that analysis. Checklists also create the possibility of becoming encumbered in specifics and therefore losing sight of the overall goal. The Supreme Court decided two decades ago that the reasonable expectation of privacy standard was a normative standard. That is, it is meant to be a reflection of an entitlement: "the standards of privacy that persons can expect to enjoy in a free and democratic society." In reaching this conclusion, the Court specifically rejected the notion that a reasonable expectation of privacy could be assessed by means of a "risk analysis." They held that "privacy would be inadequately protected if an assessment of the reasonableness of a given expectation of privacy were made to rest on a consideration whether the person concerned had courted the risk of electronic surveillance." The Court has done much to give structure to the reasonable expectation of privacy analysis since then, formulating factors in Edwards, Tessling, and more recently Patrick. At no time, however, has the Court ever suggested that its fundamental approach has changed: the reasonableness of an expectation of privacy is still to be judged normatively, not against the practical risk of being observed through technological means.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".