Bibliographic record
Abstract
On a day-to-day basis, security to most Americans means proving their identity by producing a valid government-issued identification document (ID)most commonly a drivers license. For this reason, terrorists on September 11, 2001, (9/11) placed high value on drivers licenses as a mean to mask preparatory activities leading up to their attack. Congress, as a result, enacted several measures, culminating in the Western Hemisphere Travel Initiative (WHTI), adopted June 1, 2009. The WHTI requires all citizens to show proof of identity while crossing U.S. land, sea, and recently some air borders between Canada, Mexico, the Caribbean, and Bermuda. To facilitate the initiative, the Department of Homeland Security (DHS) expanded on such ongoing ID initiatives as NEXUS, FAST, and SENTRI and adopted a number of different ID solutions, including passport card (PASS Card), Enhanced Drivers License (EDL), Global Entry and the Enhanced Tribal Card while considering others beyond the costly passport to facilitate commerce, trade, and tourism with Border States. All WHTI IDs employ vicinity-read radio frequency identification (RFID) technology, which has raised privacy concerns. This thesis seeks to join the ongoing civil liberties vs. national security debate through a case study of the EDL on both technological and legal grounds.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".