Are You on the List? Dispelling the Myth of a Total Exemption from the Privacy Act’s Civil Remedies in Shearson v. DHS
Bibliographic record
Abstract
In the post-9/11 era, Americans have increasingly come to accept that collecting private information is necessary to national security.1 Keeping the public safe undoubtedly requires the government to maintain lists of persons of interest and assess the threats such individuals pose to our national security.But what happens when something goes wrong?Julia Shearson, a Harvard graduate and a prominent interfaith leader at the Council on American-Islamic Relations, found out exactly what can happen.2 On a Sunday evening in January 2006, Ms.Shearson was returning from a road trip with her four-year-old daughter.3 Their weekend getaway to Canada was drawing to a close as they crossed the Peace Bridge approaching Buffalo, New York.4 What happened next was a traveler's nightmare.When Ms. Shearson handed her passport over to a U.S. Customs and Border Protection (CBP) agent, the agent's computer screen flashed red with the message "ARMED AND DANGEROUS."5 Ms.Shearson was ordered out of her vehicle, handcuffed, and detained for questioning.6 Her car was searched, and she was not allowed to call her family or an attorney.7 After two and a half hours of questioning, Ms.Shearson was released without
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".