Bibliographic record
Abstract
The 2016 presidential election is not the first time attention has been directed to US borders. The terrorist attacks of September 11th, 2001 and the subsequent responses provide one clear example that these borders have been a central focus for governments, businesses and citizens long before Donald Trump’s election. Anyone who has sought entry to the United States both before September 11th, 2001 and after is likely well aware of the significant differences generated by the terrorist attacks of that day. Americans citizens must now be in possession of a passport or similarly secure travel document when they leave the US in order to secure re-entry. Canadians and Mexicans must be in possession of similar documents in order to gain entry into the United States. It is even more difficult for citizens of other outside countries to enter. Security at the US borders with both Canada and Mexico increased dramatically after September 11th, 2001, visible in the expanded number of security personnel stationed at each border, the greater prominence of security apparatus, and the Congressional authorization of the construction of increased fencing along the border with Mexico. The situation is vastly different to that which pertained prior to September 11th, 2001 when Canadians could enter the US with a driver’s license and birth certificate, and indeed would often be allowed entry at land crossings with only a verbal declaration of citizenship.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".