Secure Border Initiative: DHS Needs to Follow Through on Plans to Reassess and Better Manage Key Technology Program
Bibliographic record
Abstract
Testimony issued by the Government Accountability Office with an abstract that begins "The Secure Border Initiative (SBI) is intended to help secure the 6,000 miles of international borders that the contiguous United States shares with Canada and Mexico. The program, which began in November 2005, seeks to enhance border security and reduce illegal immigration by improving surveillance technologies, raising staffing levels, increasing domestic enforcement of immigration laws, and improving physical infrastructure along the nation's borders. Within SBI, the Secure Border Initiative Network (SBInet) is a multibillion dollar program that includes the acquisition, development, integration, deployment, and operation of surveillance technologies--such as unattended ground sensors and radar and cameras mounted on fixed and mobile towers--to create a "virtual border fence." In addition, command, control, communications, and intelligence (C3I) software and hardware are to use the information gathered by the surveillance technologies to create a real-time picture of what is transpiring within specific areas along the border and transmit the information to command centers and vehicles."
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.056 | 0.010 |
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".