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. 1Keeping 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. 2On 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." 5Ms.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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".