Bibliographic record
Abstract
Abstract : The nexus among border control, illegal immigration, homeland security, and other transnational criminal activity has been a subject of much debate in the past especially since the terrorist attacks of September 11, 2001. Because much of the immigrant population residing in the United States illegally is believed to be from Mexico, Central, and South America the focus on the border between the United States and Mexico is logical. However, the border between the United States and Canada is the largest land border between nations and represents almost 4,000 miles of poorly controlled territory and is more than twice the size of the boundary shared with Mexico. The threat is greater than the seam created by the boundary or the geography. More important, perhaps, is the potentially explosive nature of the environment created by the confluence of a myriad of issues. Concerns like the mature lines of communication (LOCs) used for trafficking and smuggling between the ports of entry (POEs), a focus on cross border economic flow at entry points, and a growing number of Syrian refugees in Canada, all come together and combine with the geography to create significant security concerns.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".